china energy supply case study

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• Published: 25 September 2023

A net-zero emissions strategy for China’s power sector using carbon-capture utilization and storage

• Jing-Li Fan   ORCID: orcid.org/0000-0002-5841-6899 1 , 2 ,
• Zezheng Li 1 , 2 ,
• Xi Huang 1 , 2 ,
• Kai Li   ORCID: orcid.org/0000-0002-8464-2443 1 , 2 ,
• Xian Zhang   ORCID: orcid.org/0000-0003-3905-0489 3 ,
• Xi Lu   ORCID: orcid.org/0000-0002-5063-3776 4 , 5 ,
• Jianzhong Wu   ORCID: orcid.org/0000-0001-7928-3602 6 ,
• Klaus Hubacek   ORCID: orcid.org/0000-0003-2561-6090 7 &
• Bo Shen   ORCID: orcid.org/0000-0003-4460-0211 8  

Nature Communications volume  14 , Article number:  5972 ( 2023 ) Cite this article

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• Climate-change mitigation
• Energy modelling

Decarbonized power systems are critical to mitigate climate change, yet methods to achieve a reliable and resilient near-zero power system are still under exploration. This study develops an hourly power system simulation model considering high-resolution geological constraints for carbon-capture-utilization-and-storage to explore the optimal solution for a reliable and resilient near-zero power system. This is applied to 31 provinces in China by simulating 10,450 scenarios combining different electricity storage durations and interprovincial transmission capacities, with various shares of abated fossil power with carbon-capture-utilization-and-storage. Here, we show that allowing up to 20% abated fossil fuel power generation in the power system could reduce the national total power shortage rate by up to 9.0 percentages in 2050 compared with a zero fossil fuel system. A lowest-cost scenario with 16% abated fossil fuel power generation in the system even causes 2.5% lower investment costs in the network (or $16.8 billion), and also increases system resilience by reducing power shortage during extreme climatic events.

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Introduction.

Decarbonization of energy systems, especially the power system that accounts for up to 39.6% of global carbon emissions 1 , plays an important role in mitigating climate change. The power system will likely experience a profound transformation to achieve zero carbon emissions in the future. The latest Sixth Assessment Report of the United Nations Intergovernmental Panel on Climate Change (IPCC) states that “in pathways limiting climate warming to 1.5 °C, almost all electricity will need to rely on the supply from zero- or low-carbon sources in 2050, such as renewables or fossil fuels with carbon-capture and storage, combined with increased electrification of the energy demand” 2 . With the rapid decline in the cost of renewable power generation 3 , an extremely high proportion of renewable or even 100% renewable energy, such as solar photovoltaic (PV) and wind power, has been widely considered an effective method for future net-zero carbon power systems 4 , 5 .

Although wind and solar resources are widely available with low operating costs, their intermittent nature seriously threatens the stable and reliable electricity supply 6 , 7 . To mitigate this risk, energy storage must be widely deployed 8 . Moreover, renewables are usually unevenly distributed, and electricity load centers are often located far from supply sources, especially in large economies such as China 9 , 10 . As a result, there is an urgent need to build long-distance high-voltage infrastructures 11 , 12 to transmit surplus power from resource-rich areas to electricity demand centers. From a spatiotemporal perspective, a 100% or near-100% renewable power system may incur higher costs due to the high investment in energy storage 13 and high-voltage infrastructures 14 , 15 , otherwise suffering from a low reliability (defined as the degree to meet the ideal electricity demand under normal circumstances, with 99.9% as the current standard for Chinese cities) 4 , 9 , 11 , 12 , 16 . In addition, since wind, solar and hydropower are all climate or weather-sensitive, renewable power generation is generally regarded as one of the most vulnerable sectors to weather extremes 17 , threatening resilience (defined as the degree to meet the ideal electricity demand during weather events 18 , 19 ; see “Methods” section) of a 100% or near-100% renewable power system.

A high share of renewable power generation combined with fossil fuels involving carbon capture, utilization, and storage (CCUS) could be an alternative to 100% renewable power in the absence of a sufficient storage capacity and interprovincial transmission to ensure deep decarbonization of the future power generation system 20 , 21 and maintain system reliability and resilience. First, as one of the firm low-carbon electricity sources (e.g., nuclear power, hydropower, coal-fired power with CCUS, and natural gas-fired power with CCUS) 22 , abated fossil fuel power generation with CCUS in high-renewable power systems could partially replace variable renewable energy and lower the associated need for the construction of energy storage or high-voltage infrastructures to improve the reliability of power systems. Second, in contrast to other low-carbon power, fossil fuel power generation with CCUS is less vulnerable due to its stable thermal supply and flexibility to generate power as needed 23 . For instance, the electricity supply obtained from many nuclear and hydropower plants in France was replaced by natural gas-fired power due to the European drought in the spring and summer of 2022. Third, although CCUS currently remains expensive with a global CO 2 capture capacity of only 36.6 Mt per year in 2021 24 , its growth has been evident in recent years, with the number of demonstration projects under development or operation worldwide growing from 43 in 2018 to 136 in 2021 24 , 25 . This is reflected in the considered IPCC scenarios, with almost all integrated assessment model (IAM) scenarios incorporating CCUS under limiting global warming to 1.5 °C or 2 °C relative to preindustrial levels 26 , 27 , as the CCUS option generally yields lower costs in reducing carbon emissions than nuclear and renewable options under these scenarios 13 , 22 , 28 and provides a viable solution for carbon lock-in of fossil fuel energy infrastructure 7 , 29 , 30 , stranded assets, and industry employment losses 31 , 32 , although previous research has considered IAM-specific modeling assumptions (e.g., the application of general equilibrium theory-based IAMs) 33 . Therefore, it is important to quantify the carbon emission reduction effectiveness of the high-renewable power system combined with abated fossil fuel power generation involving CCUS via a comparison to the 100% renewable power system, especially from system reliability and resilience perspectives.

In previous research, the significance of 100% renewable or fossil fuel power generation with CCUS in future low-carbon power systems has been investigated separately. Certain scholars have emphasized the feasibility and reliability of 100% (or near-100%) renewable power systems 34 at the global 9 , 11 , 35 or national level (e.g., USA 4 , 12 and Germany 34 ). For instance, Bogdanov et al. 35 stated that a 100% renewable electricity system in 2050 is both technically and economically feasible for all regions worldwide; Dowling et al. 11 highlighted that long-duration storage (>10 h) could reduce the cost of 100% reliable wind–PV cell systems; and Brown and Botterud 4 and MacDonald et al. 12 demonstrated that interstate high-voltage transmission expansion is necessary to achieve a decarbonized power system dominated by renewables in the US at a lower cost. In contrast, IAM-based research (e.g., Rogelj et al. 36 , Jacobson et al. 37 ), in which studies depend on underlying structural constraints 33 , bottom-up industry models 13 , 21 , 22 , and power system optimization models 38 , 39 , has highlighted the inevitability of fossil fuel power generation with CCUS as a complement to renewables for deep decarbonization of the power system from an economic perspective. However, few researchers have compared the overall cost-effectiveness performance of the 100% renewable system to that of the system with a high share of renewables combined with abated fossil fuel power generation under the same system modeling framework to obtain a reliable and resilient near-zero power system, except for limited analysis from a single reliability 10 , 40 or resilience 41 , 42 perspective or comparative perspective of two individual technologies rather than under the same power system model framework 43 . More importantly, all power system optimization models in earlier studies generally lacked detailed facility and geological constraints of abated fossil fuel power plants, with notable exceptions incorporating high-resolution temporal features of variable renewable power 10 , 40 , 44 .

As a country rich in coal resources, China hosts >50% of the world’s coal-fired power generation capacity 45 , thereby emitting 37% of global power sector carbon dioxide emissions 46 . In 2020, China committed to achieving carbon neutrality by 2060 and set a target to reach a nonfossil energy consumption proportion of 80% by then 45 . Decarbonizing the power sector in China is vital for both global climate mitigation and achieving its carbon neutrality goal. Moreover, due to the unique situation in China in terms of economic development 47 , renewable endowment 9 and geological storage potential of CO 2 48 , power system strategies for other counties 4 are not directly applicable. Several recent modeling studies on China’s power system have achieved numerous advances, such as improving the resolution from yearly to hourly electricity supply–demand systems and from national to provincial levels 10 , 40 . However, they ignored the availability constraints of fossil fuel power generation with CCUS, whose potential could be high but greatly dependent on the distribution of geophysical conditions 49 , 50 , 51 . In these studies, the hourly power was aggregated into only dozens of categories due to the high computational complexity, which may cause biased results.

In this paper, we constructed a high-resolution integrated power system assessment model considering the hourly electricity supply-–demand balance by combining hourly variable renewables that vary across provinces with geologically constrained fossil fuel power generation involving CCUS, as well as energy storage and long-distance power transmission, and then applies the established model in the design of the future decarbonized electric power system architecture in China and 31 provinces (except for Hong Kong, Macau, and Taiwan) in 2050. This model integrates six interlinked modules (see Methods and Supplementary Fig.  1 ): (1) an hour-by-hour prediction model for the electricity demand in 31 Chinese provinces in 2050; (2) an hour-by-hour estimation model for the solar PV and wind power generation potential in 31 Chinese provinces; (3) a CCUS source‒sink optimal matching model for retrofitting the existing fossil fuel power plants in China; (4) an integrated simulation model configured with the hourly power system supply–demand balance in 31 provinces in 2050, which specifies energy storage duration and interprovincial power transmission capacity in combination with CO 2  geological storage-limited fossil fuel power generation involving CCUS; this model was used to analyze the reliability (i.e., by deducting the electricity shortage rate from 100%) of 10,450 combination scenarios under different storage durations, transmission capacities and shares of fossil fuel power generation with CCUS in 2050 (Supplementary Fig.  2 ); (5) a cost-competitive analysis model for the decarbonized power system in 2050, which was used to identify the lowest-cost power mix; and (6) a simulation model for the impact of representative weather extremes (snowstorms, sandstorms, droughts, and heat waves) on power generation and corresponding power shortages, which was used to analyze the resilience of the future power system.

We highlight three major findings. (1) A high proportion of renewables combined with fossil fuel power generation involving CCUS in 2050 could offset the transmission capacity and short-term storage requirements, resulting in a lower cost to achieve a certain power shortage rate (or power system reliability) relative to a zero-fossil fuel power generation system. Specifically, to achieve the lowest national total power shortage rate (the ratio of the unmet electricity demand to the ideal demand) of 0.07% with a zero-fossil fuel power generation system at five times the reference transmission level and 12 h of energy storage, 20% abated fossil fuel power generation with CCUS only requires a 3.5 times higher transmission level with 8 h of energy storage, corresponding to a 3.0% decrease in the levelized cost of energy (LCOE) relative to a zero-fossil fuel power generation system. (2) As the penetration rate of abated fossil fuel power generation technology involving CCUS increases from 0%–20% in the 2050 power generation system (as an integer), the system cost corresponding to certain reliability of 99.9% would first decrease from $679.2 (or an LCOE of 46.78 $/MWh) to $662.4 billion (or an LCOE of 45.64 $/MWh) (or by 2.5%) and then increase to $663.2 (or an LCOE of 45.69 $/MWh), with the lowest-cost power system configuration typically involving 16% abated fossil fuel power generation. (3) A high-renewable power system combined with 16% abated fossil fuel power generation involving CCUS (i.e., the lowest-cost electric power system architecture based on our 10,450 scenarios) remains more resilient under extreme weather conditions than a zero-fossil fuel power system; and if historical snowstorms, sandstorms, droughts, and heat waves were to again occur in China, power shortages in affected regions would be 54%, 56%, 57% and 68% lower, respectively, under the abated fossil fuel power generation scenario than under the zero-fossil fuel power generation scenarios. This study provides an important reference for the design of economical, reliable, and resilient near-zero power systems worldwide.

Unmet electricity demand in a zero-fossil fuel power system

By 2050, the nonfossil energy (onshore wind, offshore wind, solar PV, hydropower, and nuclear) power generation potential (equal to the sum of the corresponding hourly maximum power output potential values) in China will reach 90,076 billion kWh, of which variable renewables (solar and wind power in this study) will account for 96% or 6.2 times the total projected electricity demand (as expressed in Eq. ( 1 )) for that year (Fig.  1i , Supplementary Fig.  3a ). When the power generation potential is accounted for separately from the perspective of each province, i.e., ignoring the electricity supply from interprovincial transmission, the electricity supply–demand balance in China widely varies across provinces, with the supply-to-demand ratio ranging from 0.21~254.6 and the total unmet electricity accounting for 18.1% of the national demand (determined with Supplementary Equation (16)). Specifically, 17 of 31 provinces in China will exhibit a higher nonfossil power generation potential than their electricity demand by 2050, with supply-to-demand ratios ranging from 1.04–254.6, while the remaining 14 provinces will exhibit a deficit, with supply-to-demand ratios ranging from 0.21~0.98 (Supplementary Fig.  3b ). For instance, Guangdong, Shandong, and Jiangsu will achieve the highest electricity consumption in 2050, accounting for 26% of the national total amount, while their nonfossil power generation potential will only account for 3% of the total nonfossil power generation potential of 31 provinces, indicating high electricity supply deficiency risks. Xinjiang, Inner Mongolia, and Tibet, in contrast, will reach the highest nonfossil power generation potential (72% of the national total amount), but their electricity consumption will account for only 8% of the national total amount in 2050, resulting in a substantial electricity supply excess.

Each panel covers a different region: a North Coast; b Northeast China; c East Coast; d Beijing-Tianjin; e South Coast; f Northwest China; g Central China; h Southwest China; i Nation (the sum of 31 provinces). The cyan and orange curves in each panel denote the wind power and solar PV generation potentials, respectively, and the green curves in each panel denote the predicted electricity demand in each region in 2050. SU and WI denote summer and winter, respectively. The left-to-right columns for each region show the daily variabilities of the power generation potential and predicted electricity demand in the entire year, and the corresponding hourly variabilities in summer (June, July, and August) and winter (December, January, and February). The lines indicate the mean values, the dark shading indicates the inner 50% range (25th to 75th percentiles), and the light shading indicates the outer 50% range (0th–100th percentiles). The samples of power generation potentials are from 1980–2019 for wind and 2010–2019 for solar PV.

Further comparing the hourly nonfossil power output to the disaggregated hourly electricity demand without power transmission and energy storage, China could experience a national total power shortage rate of up to 35.4% (the sum of the power shortages in 31 provinces as a share of the national ideal electricity demand; the lower the power shortage rate, the higher the system reliability; see “Methods” section for a detailed definition), and all provinces could face power shortages, ranging from 0.4%~81.5% across the 31 provinces (Supplementary Fig.  3b ). This indicates that simply aggregating the hourly nonfossil power output could result in considerable underestimation of electricity supply shortages, especially in areas with high electricity consumption. For instance, even when several provinces are aggregated into regions, such as Beijing-Tianjin, East Coast, and South Coast, the hourly variable renewable power output is overall much lower than the electricity demand (Fig.  1c, d, e ), resulting in 8760, 7860, and 5673 h of power shortages (the sum of the hours with a lower nonfossil power generation than the electricity demand), respectively. Other regions, Northeast, Northwest, and Southwest China, could also suffer 1680, 780, and 841 h of power shortages, respectively, although they jointly account for 87.4% of the total wind power generation potential and 95.0% of the total solar power generation potential in China (Fig.  1b, f, h , respectively).

Electricity storage and power transmission, particularly their combination, could potentially lower provincial power shortages as well as national total power shortages (defined in Eq. ( 14 )). On the one hand, cross-provincial or cross-regional power transmission in China, e.g., large-scale west‒east power transmission lines, could assist in balancing spatial variations in the renewable electricity supply. For this reason, China built or planned interprovincial power transmission infrastructures with a total capacity of ~385.6 GW by 2021, including 31 ultrahigh-voltage (UHV, i.e., ±800 kV direct current and 1000 kV alternating current) lines and hundreds of other high-voltage lines (i.e., 1 kV~750 kV) 10 , 52 , 53 . By 2050, maintaining a zero-fossil fuel power system with the current transmission capacity in the absence of energy storage could reduce the national total power shortage rate in China from 35.3% to 28.1%. Measurement of the enhanced reference transmission capacity (employed as the reference scenario in this study) by artificially adding 35 UHV infrastructure channels to the current transmission capacity (Supplementary Table  1 ) could result in a national total power shortage rate of 21.8%, which could be further reduced to 11.8% with a fivefold increase in the interprovincial reference transmission capacity (Fig.  2a ). On the other hand, short- or long-term energy storage (e.g., the use of low-cost flow batteries, Li-ion batteries, compressed air energy storage, pumped hydroelectric storage, and hydrogen energy storage 8 , 11 ), particularly in renewable resource-rich areas, could stabilize intermittent local wind power and solar PV energy, despite most exhibiting a lower technology readiness level 54 , higher cost (Supplementary Table  2 ), greater geographic limitations, or lower installed capacity advancement than other low-carbon technologies such as nuclear and energy efficiency improvement. For instance, under the reference transmission scenario, allowing a maximum short-term storage capacity of 6 h could reduce the national total power shortage rate from 21.8% to 7.2%, and allowing 12 h of capacity could further reduce this value to 6.6% (Fig.  2a ).

The impact of the energy storage duration and transmission capacity on the national total power shortage rate in China in 2050 is explored by considering 10,450 scenarios with 0~24 h of short-term energy storage, 1–10 times the reference transmission capacity (0.5 intervals), and 21 abated fossil fuel share scenarios (0%~20% at 1% intervals) or zero-fossil fuel power generation with long-term energy storage. Only the results associated with durations of 0~12 h and 1~5 times the reference transmission capacity are shown in this figure, while four representative shares of abated fossil fuel power generation, namely, 0%, 5%, 10%, and 20%, are included (the long-term energy storage scenario is shown in Supplementary Fig.  6 ). The figure shows the national total power shortage rates for the various combinations of the transmission capacity and short-term energy storage duration, with the share of abated fossil fuel power generation varying in each panel: a 0%; b 5%; c 10%; d 20%. A warmer color indicates more severe power shortages, while a cooler color indicates less severe shortages. The lines denote the combination of the transmission capacity and energy storage duration for a certain power shortage level.

Nevertheless, it is challenging to overcome power shortages with either short-term energy storage or power transmission in a zero-fossil fuel power system. Specifically, the national total power shortage rate based solely on the maximum power transmission capacity and short-term energy storage, i.e., 11.8% at up to five times the reference transmission level and 6.7% at 12 h of storage (Fig.  2a ), respectively, remains well above the national critical standard for electricity supply reliability (the degree of the electricity supply meeting the electricity demand, determined as 1 minus the power shortage rate) in typical Chinese cities (99.9% or 0.1% in terms of the national total power shortage rate). The lowest national total power shortage rate of 0.07% could be obtained when the maximum short-term energy storage and power transmission are fully utilized concurrently (Fig.  2a ), but this may incur very high economic costs due to the very high capital investment cost of transmission and storage infrastructures, with relevant levelized electricity supply costs, power transmission costs, and energy storage costs of 47.24 USD/MWh, $64.7, and $71.0 billion, respectively (Supplementary Table  3 ). Interestingly, to achieve a possible lower national total power shortage rate (even if above the critical standard 0.1%) in a zero-fossil fuel power generation system, short-term energy storage and power transmission must at least reach certain capacities at the same time. For instance, to achieve a national total power shortage rate lower than 2%, the transmission capacity must be more than doubled, while the storage duration must exceed 5 h. Moreover, to realize a national total power shortage rate lower than 0.5%, these two thresholds are 2.5 times and 7 h, respectively. Overall, a fully nonfossil power system could hardly achieve satisfactory power reliability in 2050 unless short-term energy storage and transmission facilities are widely developed (as indicated by the limited colder color area representing a national total power shortage rate lower than 0.1% in Fig.  2a ) or long-term storage is included (as below).

Abated fossil fuel power generation improves power system reliability

By constructing a full-chain CCUS source‒sink matching optimization model (see “Methods” section) under the constraints of the CO 2 geological storage potential, injection rate capacity and a maximum transport distance of 500 km, as well as suitable size (≥300 MW) and remaining life (≥15 years) criteria (Supplementary Fig.  4a ), 718 of 944 coal-fired power plants (907.8 of 949.1 GW) and 58 of 165 (53.6 of 55.8 GW) natural gas-fired power plants were selected as CCUS retrofit candidates, and they were matched with 5471 storage sites (20 × 20 km 2 per site) across China, including 4926 deep saline aquifer sites and 545 oil field sites (for enhanced oil recovery (EOR)) in 17 onshore basins and 4 offshore basins (Supplementary Fig.  5a ). The resulting CCUS supply curve representing the relationship between the source‒sink distance and the cumulative installed capacity of CCUS retrofitted power plants shows that some fossil fuel power plants can access CO 2 storage sites within very short transport distances (Supplementary Fig.  4b ). For instance, 178.4 GW obtained from 141 plants could match storage sites within 100 km (Supplementary Fig.  4b ), including four oil fields that can provide extra benefits resulting from enhanced oil discovery and 137 deep saline aquifer storage sites. These infrastructure and geographical strengths provide the premise for combining a high share of renewable power with abated fossil power generation involving CCUS.

To explore the effect of abated fossil power generation technology involving CCUS on the power system reliability, we simulated 9500 scenarios with 20 fossil fuel power generation shares (1%~20%, at 1% intervals) combined with 0~24 h of short-term energy storage (at 1-hour intervals) and 1–10 times the reference transmission capacity (0.5 intervals), reaching a total of 10,450 scenarios after including the abovementioned 950 zero-fossil fuel power generation scenarios with or without long-term energy storage (Supplementary Fig.  2 and Methods). For simplicity, we only focus on the simulation results for representative abated fossil fuel power generation shares (5%, 10%, and 20%) considering a maximum short-term energy storage of 12 h and five times the reference transmission capacity since higher capacities only slightly affect the results, while the long-term energy storage scenario was used for comparison. Our simulations showed that a high share of renewables combined with abated fossil power generation involving CCUS could effectively improve the electricity supply reliability when maintaining the transmission capacity and short-term energy storage unchanged or reduce the need for transmission or the energy storage to reach the same power reliability. Under the 5%, 10%, and 20% abated fossil fuel power generation scenarios, for instance, the maximum national total power shortage rates at the reference transmission without energy storage were 19.4%, 16.9%, and 12.8%, respectively, which are 2.4%, 4.3% and 9% lower, respectively, than the value of 21.8% under the zero-fossil fuel power generation scenario (Fig.  2b–d , respectively). The minimum national total power shortage scenario, represented by five times the reference transmission capacity combined with 12 h of energy storage, yielded national total power shortage rates of 0.06%, 0.03%, and 0.03% under the 5%, 10%, and 20% abated fossil fuel power generation scenarios, respectively, which are 0.01%, 0.04% and 0.04% lower, respectively, than that under the zero-fossil fuel power generation scenario (Fig.  2b–d , respectively). In addition, to approximate the highest power system reliability under the zero-fossil fuel power generation scenario (i.e., an electricity shortage rate of 0.07% at five times the reference transmission capacity and 12 h of energy storage), only 4.5-fold transmission with 10-hour storage, 3.5-fold transmission with 8-hour storage, and 3.5-fold transmission with 7-hour storage were needed under the 5%, 10%, and 20% abated fossil fuel power generation scenarios, respectively, corresponding to 1.1%, 2.5%, and 2.8% decreases in the LCOE, respectively. These infrastructure construction cost savings for energy storage and transmission (~$9.5–$36.7 billion), combined with the potentially avoided stranded assets due to CCUS retrofitted fossil fuel power plants 55 , 56 ($4.2–$16.8 billion according to the value of 520.3$/kW·for coal-fired power plants and the value of 334.6$/kW for gas-fired power plants 53 ), represent the dual benefits of multisource power systems in China.

Optimal power system structure

The costs were then calculated for the 10,450 simulated scenarios (Methods). As shown in Fig.  3a , the minimum system cost satisfying the power reliability requirements (a national total power shortage rate of 0.1%) first decreased and then increased as the share of abated fossil fuel power generation with CCUS was increased from 0% to 20% without long-term energy storage. As a result, the 2050 power generation system in China attained the lowest cost of $662 billion, which is 2.5% lower than that of the zero-fossil fuel power system, and this power system includes 16% abated fossil fuel power generation with CCUS (14.9% for coal-fired power plants and 1.1% for natural gas-fired power plants), 30.0% variable wind power (the sum of onshore and offshore sources) and 30.5% PV power, and 12.6% hydropower, 11.1% nuclear power and 8.6% energy storage-aided generation (Fig.  3b ). To overcome the intermittent and uneven distribution of variable renewable resources, a maximum short-term storage capacity of 8 h and a transmission capacity that is 3 times the reference capacity should be adopted under the lowest-cost scenario (Supplementary Fig.  6b ). CCUS retrofitted fossil fuel plants (coal and natural gas) are concentrated in several provinces in central regions, such as Jiangsu, Henan and Hebei, but they are also scattered in the Northeast China, Northwest China and South Coast regions (Fig.  3c ), with most plants matched with storage sites between 9.7 and 146.8 km (5th to 95th percentiles), of which 13 fossil power plants in the southern coastal provinces were matched with offshore storage sites (Fig.  3c ). In the regional electricity generation composition, CCUS retrofitted fossil fuel power generation accounted for up to 68%, 40% and 31% of the total electricity supply in the Beijing-Tianjin, East Coast, and North Coast regions, respectively, corresponding to 57% of the national fossil fuel power generation involving CCUS (Fig.  3b ).

a The lowest cost of the power system and the cost composition for different CCUS retrofitted power proportions at an acceptable 0.1% national total power shortage rate, indicating that the optimal power mix should include 16% abated fossil fuel power generation with CCUS retrofitting. b Interregional power transmission under the optimal power configuration system. c Distribution of fossil power plants and their matched storage sites under the optimal power system after CCUS source-sink matching. Regional and national summer ( d June, July, and August) and winter ( e December, January, and February) power generation compositions. Data Credits: All the provincial boundaries are from the Ministry of Civil Affairs of the People’s Republic of China ( http://xzqh.mca.gov.cn/map , Map Content Approval Number: GS (2022)1873).

The line chart indicates the real-time power shortage under disasters ( a snowstorms; c sandstorms; e droughts), and the bar chart shows the composition of the electricity consumption in a 3-hour cycle under disasters ( b snowstorms; d sandstorms; f droughts). Note that b , d , f only show the electricity consumption under the optimal power system. The event times and intensities and the affected provinces are sourced from actual disasters in China, i.e., 14 January to 4 February 2008, for snowstorms, 15–17 March, 2021, for sandstorms, and 12–27 August 2022, for droughts.

Our simulations revealed a specific optimal electricity supply structure (without considering long-term energy storage) in each province with matched hour-by-hour interprovincial transmission or at any downscaling level. For instance, at the regional level, the Northwest and Southwest China regions, with high variable renewable power generation potential, could supply up to 2196 and 313 TWh of electricity, respectively, to other regions while meeting their own electricity demand, accounting for 81% and 11%, respectively, of the total interregional transmission (Fig.  3b ). Regarding seasonal changes, the Northeast and Northwest China regions generate less electricity in summer than in winter (−8.4% and −7.6%, respectively), while other regions, conforming with national features 57 , 58 , generate more electricity in summer than in winter (2.6% to 26.7%, respectively). This occurs because more space heating (by electricity) is needed in the Northeast and Northwest China regions due to the cold winter weather conditions, in addition to the need for higher transmission power to the eastern regions where renewable sources are scarce in winter (Fig.  1a, c, g ). Moreover, almost all regions exhibit higher solar PV power generation in summer than in winter, with seasonal differences ranging from 0 to 91% (national: 37%), and higher wind power generation in winter than in summer, ranging from 17 to 41% (national: 27%).

Long-term energy storage technology (e.g., hydrogen and thermal energy storage) may play an essential role in sustaining electricity supply reliability, similar to the role of fossil fuel power generation with CCUS. We simulated a set of scenarios considering a zero-fossil fuel power system with long-term hydrogen storage for comparison. The results showed that a minimum combination of the transmission capacity and short-term energy storage is required to ensure a relatively low power shortage rate under the zero-fossil fuel power system with long-term hydrogen storage, similar to all other scenarios. For instance, meeting the 0.1% national total power shortage rate with the lowest cost necessitates the construction of facilities providing 6 h of short-term energy storage and 4 times the reference transmission capacity (Supplementary Fig.  6a ), corresponding to a system cost of $820 billion and a levelized cost of 47.15 USD/MWh, which are 19.3% and 3.2% higher, respectively, than the high-renewable scenario with 16% abated fossil fuel power generation (Supplementary Fig.  7 ). However, if the national electricity supply reliability standard were further enhanced (i.e., lower than 0.1% of power shortage), long-term energy storage would play a more important role than abated fossil fuel power generation involving CCUS, as shown by the larger area towards the right-upper corner representing the same power shortage level in Supplementary Fig.  6a than Fig.  6b , or even result in a lower levelized cost to meet the higher power shortage rate standard, e.g., with at lowest 47.19 and 48.21 USD/MWh to achieve a 0.01% national total power shortage rate under the zero-fossil fuel power system with long-term energy storage and high-renewable scenario with 16% abated fossil fuel power generation, respectively. Therefore, long-term energy storage technology is a wise option to improve the power system supply reliability in the face of more stringent national shortage rate standards in the future.

Power system resilience to extreme climatic events

Considering the vulnerability of variable renewable energy to weather variability (e.g., wind speed and irradiance), we measured the power system resilience to historical extreme climatic events by simulating and comparing the impacts of snowstorms, sandstorms, droughts, and heat waves on power shortages under power systems using zero-fossil fuel power generation and a high share of renewables combined with 16% abated fossil fuel power generation involving CCUS (i.e., the lowest-cost scenario). As shown in Fig.  4 , both types of power systems are likely to be affected by these extreme climatic events, but the impact would be much less in the case of a high share of renewables combined with abated fossil power generation involving CCUS.

In the case of snowstorms, the affected areas would exhibit a total power shortage rate over 10% of 396 h during the disaster period under the zero-fossil fuel power system, with a maximum single-hour power shortage rate of 44%, accounting for 75% of the main affected period (January 14-February 4), seriously affecting the power grid stability (Fig.  4a ). With a high share of renewables combined with 16% abated fossil fuel power generation, the occurrences of hourly power shortage rates over 10% would decrease to 101, accounting for just 19% of the main affected period with a maximum single-hour power shortage rate of 16% (Fig.  4a ). Correspondingly, a high share of renewable power combined with 16% abated fossil fuel power generation could yield a much lower total power shortage of 10.4 TWh in affected provinces (i.e., regional power shortages), decreasing by 22.9 TWh or 54% relative to the zero-fossil fuel power system (Supplementary Fig.  8b ). At the provincial level, the abated fossil fuel power plants with CCUS were more effective for Anhui, Hebei, and Guangxi than other affected provinces in alleviating the power shortages under zero-fossil fuel power system, decreasing the provincial power shortage rate from 48%, 9.5%, and 4% to 0.03%, 1.58%, and 1.04%, respectively (Supplementary Fig.  8a ).

In comparison, sandstorms and droughts are less likely to impact the power system in terms of both the occurrence of adversely affected hours with a power shortage rate over 10% (8 and 18 h, respectively) and total power shortages (0.9 and 2.1 TWh, respectively) in the affected provinces (i.e., regional power shortages), but a high share of renewables combined with an abated fossil fuel power generation system is more resilient than a zero-fossil fuel power system (Fig.  4c, e ). For instance, the areas affected by sandstorm and drought events would experience 5 and 13 more hours with hourly power shortage rates over 10% under the zero-fossil fuel power system than under the high-renewable power system with abated fossil fuel power generation, and the regional total power shortages could be reduced by 56% and 57% during these events (from 0.9 to 0.4 TWh and 2.1 to 0.9 TWh, respectively) under the abated fossil fuel power generation system, respectively (Fig.  4d, f and Supplementary Fig.  8d, f ). Notably, electricity shortages due to sandstorms will not occur until 16 March under the zero-fossil fuel power system although these weather events started earlier on 15 March, and in the case involving a high share of renewables combined with abated fossil fuel power generation, the start of severe electricity shortages could be further delayed by ~7 h (Fig.  4c ). This is mainly due to the strong winds associated with these sandstorms initially increasing wind power for energy storage, which could stabilize the electricity supply for several additional hours, especially when fossil fuel power was included in the system. At the provincial level, Xinjiang, Gansu, and Ningxia under sandstorms and Sichuan under drought events exhibited more effective than other affected provinces in alleviating the power shortages under zero-fossil fuel power system, with the cumulative power shortage rates decreasing from 6.61%, 6.47%, 41.93%, and 5.4% to 0%, 4.57%, 19.0%, and 3.0%, respectively (Supplementary Fig.  8c, e ). Heat waves exerted the least impact on the electricity supply system under either the zero-fossil fuel power generation system or the abated fossil fuel power generation with CCUS system (Supplementary Figs.  8g , h and 9 ).

Importantly, adopting abated fossil fuel power generation could alleviate extreme power shortages at certain moments during weather events. For instance, the highest hourly power shortage rates in the zero-fossil fuel power system during snowstorms (43.9%), sandstorms (28.8%), and droughts (38.3%) may be reduced by 33.2%, 14.9%, and 38.3%, respectively, under a 16% abated fossil fuel power generation system (Fig.  4a, c, e ). Nevertheless, a high proportion of renewables combined with CCUS retrofitted fossil fuel power generation remained adversely affected almost throughout the entire 24 h a day during snowstorms and sandstorms. As shown in Fig.  4b, d , power shortages were observed throughout the day and night during snowstorms, with the power shortage rate peaking at 21–24 pm, accounting for up to 9.1% of total electricity demand during this period (Fig.  4b ), while the most severely affected periods during sandstorms mainly ranged from 0–12 am, with the 6–9 am period exhibiting the highest power shortage rate of up to 4.9% of total electricity demand during this period (Fig.  4d ). However, power shortages during sandstorms from 12 am to 6 pm were negligible, mainly because the extra solar power stored in the morning could be discharged in the afternoon to meet the electricity demand even though these sandstorms severely affected the PV output. In contrast to snowstorms and sandstorms, a high proportion of renewables with abated fossil fuel power generation under droughts could generate slight power shortages, and the power shortage rate over 1% only occur from midnight to 3 am and 18–24 pm, with a maximum value up to 3.6% from 21–24 pm (Fig.  4f ). These results were confirmed by a similar performance in the zero-fossil fuel case (Supplementary Fig.  8f ).

In this study, we constructed a high-resolution comprehensive simulation model for hourly power system optimization and applied it to evaluate deep decarbonization options for China’s power system in 2050. We compared the impacts of various systems with a high share of renewables combined with abated fossil fuel power generation involving CCUS to that of the zero-fossil fuel power system on electricity supply reliability and resilience. In order to minimize electricity supply shortages at a very high temporal resolution, the model configuration considers future power system compositions based on the estimated hour-by-hour power output potential of different power source types, as well as the predicted hourly electricity demand in 31 Chinese provinces in 2050. Then, 10,450 scenarios based on various combinations of short-term energy storage duration, transmission capacity, and share of abated fossil fuel power generation or use of long-term energy storage were simulated to configure the electricity supply structure considering electricity shortages and corresponding system costs. After determining the lowest-cost high-renewable power structure with abated fossil fuel power generation, we further simulated the impacts of extreme climate events on power shortages under the zero-fossil fuel power system and a 16% abated fossil fuel power generation system. Through these simulations, the optimal power structure considering both reliability and resilience could be derived, which is useful for the Chinese power sector to develop long-term decarbonization pathways toward the 2060 carbon neutrality goal.

As the carbon dioxide capture rate does not reach 100%, typically up to 90% for fossil fuel power generation-related flue gas 59 , the optimal high-renewable share power system configured in this study cannot achieve net-zero emissions via sole reliance on fossil energy power generation with CCUS. A simple and feasible way to achieve a net-zero power system is to co-fire fossil fuels and biomass energy sources at CCUS retrofit-ready plants to offset the uncaptured CO 2 benefits from the negative emissions of bioenergy with CCUS (BECCS). By simulating the optimal matching of surrounding biomass resources (agriculture residues, forest residues, and energy plants) with candidate CCUS fossil fuel-fired power plants, we found that 166 out of 196 candidate CCUS fossil fuel-fired power plants could be matched with biomass resources within a 50-km radius (Supplementary Fig.  10 ). The optimal power system could achieve net-zero emissions at an average co-firing ratio of 13% (see “Methods” and Supplementary Fig.  10 ). Based on available engineering experiences and research evidence 60 , 61 , a co-firing ratio of less than 20% could result in very low costs, as no retrofitting of coal-based boilers is needed. Thus, China could achieve a net-zero power system by 2050 as a result of the partial mitigation contribution of BECCS associated with biomass and coal co-firing. Furthermore, if existing fossil fuel-fired power plants could be fully converted into dedicated biomass-fired power plants with sufficient biomass resources and geological sequestration, this system could achieve considerable net negative emissions. Following the lead of selected developed countries (e.g., the 45Q credit system in the US and carbon taxes in Norway) 32 , China’s incentive policies for CCUS, as well as biomass and coal co-firing with CCUS, should be enhanced to promote deployment at fossil fuel-fired power generation facilities.

Finally, differentiated measures should be implemented in the various Chinese regions (provinces) according to their dependence on cross-regional (interprovincial) power transmission. At the regional level (Supplementary Fig.  11a , b ), the Beijing-Tianjin and East Coast regions are net electricity importers with the highest external electricity dependence (i.e., the imported electricity as a percentage of domestic electricity consumption) at 40% and 32%, respectively, while the Northwest, Southwest and Northeast China regions are net electricity exporters, with exported electricity exceeding domestic consumption levels of 109%, 16% and 8%, respectively. At the provincial level (Supplementary Fig.  11c , d ), Chongqing and Shanghai have the highest external dependence, at 71% and 60%, respectively, while Xinjiang and Inner Mongolia are net electricity exporters, with exported electricity exceeding domestic consumption levels of 176% and 144%, respectively. Overall, China must construct more long-distance transmission infrastructures in the different regions or provinces with high external electricity dependence while prioritizing different energy storage technology options, including short- and long-term energy storage systems, in regions or provinces that are net electricity exporters. Moreover, the electricity supply mix in these net electricity exporter regions is dominated by variable renewable energy sources, making them more vulnerable to extreme climate events. Therefore, local governments in these areas should enhance the adaptability of their power systems by implementing proactive measures such as grid reinforcement, long-term energy storage, or backup fossil energy power generation to provide an emergency electricity supply.

Research framework

In this paper, we constructed an integrated model comprising six modules that correspond to the six steps of the research framework (Supplementary Fig.  1 ). In the first step, the real-time hourly potential of the nonfossil power output in 31 Chinese provinces was estimated. A downscaling approach combining historical hour-by-hour climate information and different engineering calculations was used to downscale the aggregate annual wind and solar PV power output potentials in each province to the real-time hourly level. The installed capacity potential of nuclear power and hydropower in 2050 was month-adjusted from existing estimates. In the second step, the hourly electricity demand in each province in 2050 was predicted. In this step, an econometric model was first developed and then downscaled to the hourly level, i.e., the hourly electricity demand in each province in 2050 was projected by combining datasets of the typical workday and non-workday electricity loads for each province with the hourly electricity load in a typical reference province. In the third step, a CCUS source‒sink matching model was developed that could be used to identify the optimal links between fossil plants and suitable storage sites under a given fossil fuel share of electricity consumption. In the fourth step, an integrated optimal near-zero power system simulation model was established to assess the reliability associated with the real-time hourly electricity demand. In general, the real-time hourly electricity demand could be satisfied through four electricity supply sources, i.e., the local real-time hourly electricity supply, real-time hourly electricity dispatch, energy storage discharging, and  power transmission from other provinces via energy storage discharging. In this step, we simulated 10,450 cases combining different transmission capacities, power storage durations (including short-term energy storage ranging from 0 to 24 h and long-term energy storage without energy storage duration limitations), and specified shares of fossil fuel power generation to assess their reliability under the framework of a near-zero or net-zero power system, as well as the relationships with the abated fossil power generation share. In the fifth step, a cost-competitive analysis of the different scenarios was performed to finally determine the lowest-cost power system composition in China in 2050. In the sixth step, the effect of abated fossil power generation with CCUS on the resilience of power system was examined by characterizing and comparing the impacts of typical climate events on CCUS retrofitted fossil fuel power generation systems and the zero-fossil power generation system.

Assessment of the nonfossil fuel power potential

The hourly power output potential of onshore wind, offshore wind, solar PV, and stable nonfossil energy sources was projected separately. A downscaling approach based on real-time hourly climate information for recent decades was used to refine the provincial variable renewable output potential to the hourly scale. The specific prediction methods for each electricity supply source are described in Supplementary Note  1 .

Assessment of abated fossil fuel power generation with CCUS

Referring to Fan et al. 62 , we developed a CCUS source‒sink matching model based on a multiobjective optimization model with extended CO 2 emission sources from existing coal-fired power generation plants to existing coal- and gas-fired power plants. We also updated the storage site database in this study by expanding onshore storage sites to onshore and offshore storage sites. After source‒sink matching, we obtained the distribution of power plants that could be prioritized for CCUS project retrofits and used this distribution to determine the maximum hourly generation potential in each province for different fossil fuel shares, with the data and assumptions referenced in Supplementary Note  2 .

Hourly electricity demand predictions by province in 2050

In this study, the electricity demand in 2050 was projected based on econometric models, accounting for different future socioeconomic development scenarios. Considering the future demand-side response (lowering the actual electricity demand) and capacity margin requirements (increasing the actual grid generation demand), a lower total demand scenario was chosen, suggesting that the demand response effect is greater than the capacity margin effect. Under this scenario, the total future electricity demand in China in 2050 would reach 14.53 trillion kWh, as expressed in Eq. ( 1 ).

where NEIED is the national entire ideal electricity demand in 2050 (PWh), \({E}_{n}\) is the predicted electricity consumption per-capita in 2050 in province n (PWh), \(P{S}_{n}\) is the population share of province n (%), and \(NEP\) is the national total population in 2050.

In regard to annual provincial electricity demand prediction, a fixed-effects multiple regression model using the per-capita electricity consumption as the dependent variable was developed based on electricity consumption data for 30 Chinese provinces from 1995 to 2019. Then, the per-capita electricity demand in 2050 in each province was predicted according to the model estimations and the future projections of the independent variables. Combined with the future predicted population of China, the final projected electricity demand in 2050 in each province was further calculated. The econometric model is expressed in Eq. ( 2 ).

where \({E}_{n,y}\) is the electricity consumption per-capita in year y in province n (PWh), \(GD{P}_{n,y}\) is the gross domestic product (GDP) per-capita in year y in province n at 1990 constant prices (USD), \(HD{D}_{n,y}\) is the value of the heating degree days in year y in province n (°C·d) (as expressed in Eq. ( 3 )), \(CD{D}_{n,y}\) is the value of the cooling degree days in year y in province n (°C·d) (as expressed in Eq. ( 4 )), SI n,y is the ratio of the value added of the secondary industry to the GDP in year y in province n (%), \(EP{I}_{n,y}\) is the electricity price index in year y in province n based on the consumption price index of the electricity and heat producing industry, \({\beta }_{1}-{\beta }_{5}\) are the regression coefficients of each independent variable, and \(\varepsilon\) is the random error term.

where \({T}^{t}\) is the temperature threshold (°C), and \({T}_{d,n,y}\) is the average daily temperature on day d in year y in province n (°C).

Compared to the literature on electricity demand predictions with Chinese power system models 40 , 54 , 63 , we used a more accurate and refined method to predict the hourly electricity demand in each province in China in 2050.

First, according to the monthly electricity consumption and the hourly electricity load on typical workdays and nonworkdays in each province in 2019 (the representative provinces in the eight regions are shown in Supplementary Fig.  12 ), the corresponding hourly electricity load on workdays and nonworkdays in each month were calculated by Eqs. ( 5 )–( 6 ). Second, considering the number of workdays and nonworkdays in each month, the average hourly baseline electricity load at the same hour in each month was obtained by Eq. ( 7 ). Third, due to the data availability, Anhui Province was selected as the reference province, and its hourly actual electricity consumption in 2019 was used to calculate the proportion of the electricity load in each hour of each day in each month relative to the electricity load in the same hour of the same month, as expressed in Eq. ( 8 ). Then, the hourly electricity demand in each province for the whole year was calculated referring to the hourly electricity demand variation proportion in Anhui Province determined by Eq. ( 9 ). Finally, the real-time hourly electricity demand in each province in 2050 was predicted using the multiplicator of the annual provincial electricity demand relative to 2019. The specific equations are described below.

The hourly electricity load on workdays and nonworkdays in each month in each province is expressed in Eqs. ( 5 )–( 6 ).

where \(E{C}_{n,m,t}^{w}\) is the electricity consumption at hour t on workdays of month m in province n (MWh), \(TE{L}_{n,t}^{w}\) is the typical electricity load at hour t on workdays in province n (MW), \(E{C}_{n,m}\) is the electricity consumption in month m in province n in 2019 (MWh), \({D}_{m}^{w}\) is the number of workdays in month m , \({D}_{m}^{nw}\) is the number of nonworkdays in month m , \(E{C}_{n,m,t}^{nw}\) is the electricity load at hour t on nonworkdays in month m in province n (MWh), and \(TE{L}_{n,t}^{nw}\) is the typical electricity load at hour t on nonworkdays in province n (MW).

The average hourly baseline electricity load in each province can be obtained with Eq. ( 7 ).

where \(AE{C}_{n,m,t}\) is the average electricity consumption at hour t in month m in province n (MWh).

The variation ratio of the actual hourly electricity consumption to the average electricity consumption in Anhui Province is expressed in Eq. ( 8 ).

where \(P{P}_{m,d,t}\) is the variation proportion of the actual electricity consumption in Anhui Province in 2019 to the average electricity demand at hour t on day d in month m and \(E{C}_{m,d,t}\) is the actual electricity consumption in 2019 in Anhui Province at hour t on day d in month m (MWh).

The hourly electricity demand in the other provinces is defined in Eq. ( 9 ).

where \(E{D}_{n,m,d,t}\) is the electricity demand in province n at hour t on day d in month m (MWh).

Finally, the real-time hourly electricity demand in 2050 in each province can be estimated using the multiplicator derived from the provincial electricity demand in 2050 relative to 2019 estimated by econometric models.

Optimal near-zero power system simulation model

In this paper, an optimal near-zero power system simulation model was established, which incorporates the 2050 hourly electricity demand, nonfossil fuel power output potential predictions, and the optimal layout of CCUS source‒sink matching for fossil fuel power plants, as well as power transmission and energy storage (including short- and long-term energy storage). This model was calculated using MATLAB software. Since the future electricity supply structure in China will fundamentally differ from the current one, 35 new interprovincial transmission routes was added to the existing 50 ones, i.e., a total of 85 transmission routes, comprising the reference transmission capacity under the scenario framework (Supplementary Table  1 ).

Considering the uncertainty in the future near-zero power system structure, we considered a total of 10,450 scenarios (19 × 25 × 22) in this study based on different transmission capacity times (1–10 times at 0.5 intervals), short-term energy storage durations (0–24 h at 1-hour intervals 9 ), and abated fossil fuel shares (0%–20% as an integer) or zero-fossil fuel with long-term energy storage for comparison (the inclusion mechanism for long-term energy storage is described in the Supplementary Note  3 ). On this basis, the hourly energy mix and power shortage in each province were simulated under different scenarios.

Power shortage rate definition

In this study, we defined four categories of power shortage rates, including the national total power shortage rate, provincial total power shortage rate, national hourly power shortage rate, and provincial hourly power shortage rate, as described below.

First, the national total power shortage rate represents the cumulative gap between the provincial hourly electricity supply not meeting the ideal hourly electricity demand divided by the national overall ideal electricity demand, as expressed in Eq. ( 10 ).

where \(TP{S}^{N}\) is the national total power shortage rate, \(E{S}_{n,t}\) is the electricity supply at hour t in province n , including the local real-time hourly electricity supply via power generation, real-time hourly dispatch electricity supply via power generation, local energy storage discharging electricity supply, and hourly dispatch via energy storage discharging electricity supply, as expressed in Eq. ( 15 ), and \(IE{D}_{n,t}\) is the ideal electricity demand in 2050 at hour t in province n , as defined in Eq. ( 9 ).

Second, the provincial total power shortage rate represents the cumulative gap in a specific province between the hourly electricity supply not meeting the ideal hourly electricity demand divided by the provincial overall ideal electricity demand, as expressed in Eq. ( 11 ).

where \(TP{S}_{n}^{P}\) is the total power shortage rate in province n .

Third, the national hourly power shortage rate represents the gap between the national hourly electricity supply not meeting the ideal hourly electricity demand divided by the national ideal hourly electricity demand, as defined in Eq. ( 12 ).

where \(HP{S}_{t}^{N}\) is the national hourly power shortage rate at hour t .

Finally, the provincial hourly power shortage rate represents the gap in a specific province between the hourly electricity supply not meeting the ideal hourly electricity demand divided by the provincial ideal hourly electricity demand, as defined in Eq. ( 13 ).

where \(HP{S}_{n,t}^{P}\) is the hourly power shortage rate at hour t in province n .

Assumptions and configuration of the power system simulation model

In this study, an optimal simulation model for the future near-zero power system was constructed involving seven power generation technologies (nuclear power, hydropower, onshore wind, offshore wind, solar PV technology, coal-fired power with CCUS, and natural gas-fired power with CCUS), and four electricity supply sources of the local real-time hourly power output, real-time hourly dispatch, local energy storage discharging, and power transmission from other provinces via energy storage discharge (Supplementary Fig.  2 ). To ensure a more realistic power system composition and to simplify the modeling process, the following assumptions were made:

Due to the difficulty of obtaining grid transmission lines within a province and simplifying the model to ensure a manageable optimization issue, the power system simulations only considered interprovincial power transmission, which enabled each province to be regarded as a single node. Moreover, the same type of power generation unit within a given province was considered a single unit.

Considering the grid preference for various electricity supply sources and the cost of power generation technology, the power system was assumed to prioritize the use of steady-state power sources, and thus, the priority for the adoption of the various power generation technologies was nuclear power > hydropower > abated fossil fuel power > variable renewable energy power (including onshore wind power, offshore wind power, and solar power).

Energy storage was classified as short-term (within 24 h) and long-term (without time constraints) energy storage. Due to the higher cost of long-term energy storage, priority was given to short-term energy storage discharge while supplying electricity.

Considering the different costs of the various electricity supplies, the priority for the use of the electricity supply in each province was as follows: real-time hourly electricity supply from local power generation > real-time hourly dispatch electricity supply via power generation > local short-term energy storage discharging > hourly dispatch via short-term energy storage discharging > local long-term energy storage discharging > hourly dispatch via long-term energy storage discharging.

To reduce power shortages, real-time hourly dispatch was prioritized for supplying the province with the most severe power shortages where the local real-time hourly electricity supply cannot meet the provincial electricity demand and transmission lines are available.

To improve the number of utilization hours and increase power generation in provinces with better resource conditions, provinces with the highest remaining variable renewable energy power generation potential after meeting their local real-time hourly electricity supply and real-time hourly dispatch were prioritized for energy storage discharging and subsequent dispatch.

In terms of short-term energy storage charging, only variable renewable energy storage was examined, and different storage durations were assumed as the upper limit for continuous energy storage charging in each province, without considering the time constraint of energy storage discharging.

The objective of the optimal near-zero power system simulation model was to ensure a minimum national shortage rate under the scenarios with different abated fossil fuel shares, transmission capacities, and storage durations. The objective is defined in Eq. ( 14 ).

where \(NPS\) is the national power shortage (MWh), and \(E{S}_{n,t}\) includes four electricity supply sources, as expressed in Eq. ( 15 ).

where \(E{S}_{n,t}^{l}\) is the local real-time hourly electricity supply via local power generation at hour t in province n , \(E{S}_{n,t}^{d}\) is the real-time hourly electricity supply from other provinces via power generation to province n at hour t through hourly dispatch, \(E{S}_{n,t}^{s}\) is the hourly electricity supply from energy storage discharging at hour t in province n , and \(E{S}_{n,t}^{sd}\) is the hourly electricity supply from other provinces to province n at hour t through hourly dispatch via energy storage discharging.

The overall constraint of an optimal near-zero power system is to ensure that the hourly electricity supply is lower than the electricity demand, as determined in Eq. ( 16 ).

where \(E{S}_{n,t}\) is the total electricity supply at hour t in province n , including four electricity supply sources. Based on the hourly electricity supply sources, the optimal near-zero power simulation model was divided into four modules: local real-time hourly electricity supply via power generation module, real-time hourly dispatch electricity supply via power generation module, local energy storage discharging electricity supply module, and hourly dispatch via energy storage discharging electricity supply module. Note that energy storage in the above modules does not include long-term energy storage. The main constraints of the four modules are as follows:

First, in the local real-time hourly electricity supply via power generation module, local power generation was prioritized to meet the local electricity demand, with the main constraints including the following: the local real-time hourly electricity supply via power generation in each province must not exceed its electricity demand, and it must not exceed the total power generation potential of local power generation technologies, as expressed in Eqs. ( 17 ) and ( 18 ), respectively.

where \(PG{P}_{z,n,t}\) is the power generation potential of the various power generation technologies z at hour t in province n , where \(z=1\) is onshore wind power, \(z=2\) is offshore wind power, \(z=3\) is solar PV power, \(z=4\) is nuclear power, \(z=5\) is hydropower, \(z=6\) is coal-fired power with CCUS, and \(z=7\) is natural gas-fired power with CCUS.

Second, if the local real-time hourly electricity supply from power generation is insufficient to meet the local real-time hourly electricity demand, the real-time hourly dispatch electricity supply via the power generation module will be needed, with the main constraints including the following: the dispatched electricity should not exceed the local unmet electricity demand by local power generation; the maximum dispatched electricity along each route is constrained by its designed transmission capacity, and it should not exceed the upper limit of that available from the outflow province, as expressed in Eqs. ( 19 )–( 21 ).

where \({X}_{n^{\prime},n}\) is a binary variable and is assigned a value of 1 if there is a transmission line from dispatched outflow province n’ to dispatched inflow province n . Otherwise, a value of 0 is assigned. \(D{C}_{n^{\prime},n}\) is the maximum transmission capacity from dispatched outflow province n’ to dispatched inflow province n , \(PG{P}_{z,n^{\prime},t}\) is the power generation potential of the various power generation technologies z at hour t in province \(n^{\prime}\) , \(IE{D}_{n^{\prime},t}\) is the ideal electricity demand in 2050 at hour t in province \(n^{\prime}\) , and \(ED{S}_{n^{\prime},n,t}^{d}\) is the accumulation of electricity dispatch via power generation from province \(n^{\prime}\) to the other provinces prioritized over province \(n\) at hour t (if province \(n\) is the most electricity-deficient province and all other provinces are prioritized to supply electricity to province \(n\) , \(ED{S}_{n^{\prime},n,t}^{d}\) is 0).

Third, if the local real-time hourly electricity supply from power generation and real-time hourly dispatch electricity supply from power generation in other provinces still cannot meet the local electricity demand, the local energy storage discharging electricity supply module will be needed, with the main constraints including the following: the local energy storage discharging electricity supply should be lower than the remaining electricity demand after local hourly power generation and dispatch electricity supply and should be less than the amount of energy storage charging minus the amount of electricity discharged, as expressed in Eqs. ( 22 ) and ( 23 ), respectively.

where \(h\) is an auxiliary variable related to \(t\) for simulating the process of energy storage charging and discharging, \(h0\) is the number of hours from the end of energy storage charging to hour t , \(H\) is the maximum number of energy storage hours, which ranges from 1 to 24 hours according to the different scenarios, \(RW{P}_{n,h}\) is the remaining power generation potential of wind power after the local real-time hourly electricity supply and real-time hourly dispatch electricity supply at hour \(h\) in province n , \(RP{V}_{n,h}\) is the remaining power generation potential of solar PV power after the local real-time hourly electricity supply and real-time hourly dispatch electricity supply at hour \(h\) in province n , \(E{S}_{n,h}^{s}\) is the local energy storage discharging electricity supply at hour h in province n , and \(ED{S}_{n,h}^{sd}\) is the electricity dispatch via energy storage discharging from province n at hour h .

Fourth, if all the above electricity supply sources cannot meet the local electricity demand, dispatch via energy storage discharging electricity supply module will be needed, with the main constraints including the following: the dispatch via energy storage discharging electricity supply should not exceed the local remaining electricity demand, should not exceed the remaining capacity of each transmission line, and should not exceed the amount of energy storage charging minus the amount of electricity discharged in the electricity outflow provinces, as expressed in Eqs. ( 24 )–( 26 ).

where \(E{S}_{n^{\prime},n,t}^{d}\) is the real-time hourly dispatch electricity supply via power generation from province \(n^{\prime}\) to province n at hour t , \(RW{P}_{n^{\prime},h}\) is the remaining power generation potential of wind power after the local real-time hourly electricity supply and real-time hourly dispatch electricity supply at hour \(h\) in province \(n^{\prime}\) , \(RP{V}_{n^{\prime},h}\) is the remaining power generation potential of solar PV after the local real-time hourly electricity supply and real-time hourly dispatch electricity supply at hour \(h\) in province \(n^{\prime}\) , \(E{S}_{n^{\prime},h}^{s}\) is the local energy storage discharging electricity supply at hour h in province \(n^{\prime}\) , \(ED{S}_{n^{\prime},h}^{sd}\) is the electricity dispatch via energy storage discharging from province \(n^{\prime}\) at hour h , \(E{S}_{n^{\prime},t}^{s}\) is the local energy storage discharging electricity supply at hour t in province \(n^{\prime}\) , and \(ED{S}_{n^{\prime},n,t}^{sd\_\circ }\) is the accumulated electricity dispatch via energy storage discharging dispatched from province \(n^{\prime}\) to province n at hour t . Specific calculations are provided in the Supplementary Note  3 .

Cost-competitive analysis of the near-zero power system

To evaluate the economics of the power system, we calculated the costs of the overall power system and its components under all scenarios and selected the optimal scenario characterized by the lowest total cost and total power shortage lower than 0.1% (i.e., ensuring a general level of the electricity supply reliability of 99.9% in Chinese cities). The total cost of the power system includes the cost of nonfossil fuel power generation, the cost of abated fossil fuel power generation with CCUS, the cost of short-term energy storage, the cost of hydrogen energy, and the cost of power transmission (all costs in this study were adjusted to 2020 constant prices), as expressed in Eq. ( 27 ).

where \(COST\) is the total cost of the power system (at 2020 constant prices) (USD), \(LCO{E}^{nc}\) , \(LCO{E}^{hp}\) , \(LCO{E}^{pv}\) , \(LCO{E}^{on-wp}\) , \(LCO{E}^{off-wp}\) , \(LCO{E}^{cpccs}\) , \(LCO{E}^{ngccs}\) , and \(LCO{E}^{es}\) are the LCOEs of nuclear power, hydropower, solar PV power, onshore wind power, offshore wind power, coal-fired power with CCUS, natural gas-fired power with CCUS, and short-term energy storage in 2050 (USD/kWh), respectively, \(E{C}^{nc}\) , \(E{C}^{hp}\) , \(E{C}^{pv}\) , \(E{C}^{on-wp}\) , \(E{C}^{off-wp}\) , \(E{C}^{cp}\) , \(E{C}^{ng}\) , \(E{C}^{es}\) , and \(E{C}^{H2}\) are the total electricity consumption levels of nuclear power, hydropower, solar PV power, onshore wind power, offshore wind power, coal-fired power with CCUS, natural gas-fired power with CCUS, short-term energy storage, and hydrogen in 2050 (kWh), respectively (under the long-term energy storage scenario, the additional consumption of additional variable renewable electricity due to the production of hydrogen is captured by \(E{C}^{pv}\) , \(E{C}^{on-wp}\) , and \(E{C}^{off-wp}\) ), \(H{2}^{uc}\) is the unit production cost of hydrogen, \(PE{C}^{H2}\) is the proportion of the electricity costs in hydrogen production (%), \(H{2}^{s}\) is the cost of hydrogen storage in 2050 (USD/kg), H 2 c is the hydrogen consumption in 2050 (kg), \(LCO{E}^{H2}\) is the LCOE of the electricity generated from hydrogen in addition to the cost of fuel in 2050 (USD/kWh), I is the candidate transmission route in this study, with a total of 85, \(COS{T}^{UT}\) is the unit transmission cost (USD/km·GW), \(L{C}_{I}\) is the length of power transmission route I (km), and \(Ca{p}_{I}^{T}\) is the maximum hourly utilization capacity of route I (GW). Detailed cost data are provided in Supplementary Table  5 . The cost calculations of nonfossil fuel power generation, abated fossil fuel power generation, short-term energy storage, hydrogen energy, and power transmission are presented in Supplementary Note  4 . The LCOE of the near-zero-carbon power system was obtained by dividing the total levelized cost of the power system by the electricity consumption in 2050.

Modeling the impacts of extreme weather events

We then investigated the effects of extreme weather events (snowstorms, sandstorms, droughts, and heat waves) on the overall system resilience under the near-zero power system while incorporating abated fossil fuel power generation with CCUS. The 2008 snowstorm in southern China was chosen as a reference disaster since it was the most severe and widespread rain, snow, and freezing natural disaster in China since 2000. The 2021 sandstorm in northern China was adopted as another reference disaster since it was the most powerful and extensive sandstorm in China in the previous decade. Since 2022, heat waves and droughts have become more extreme climate crises plaguing the Chinese power grid. The 2022 heat wave in Southeast and Northwest China showed the highest intensity since the 21st century, and the 2022 drought seriously impacted southern China. Therefore, these two disasters were also introduced as representative extreme weather events. The mechanism of the impact of each event on the power system is as follows:

The simulation of the 2008 snowstorm impact incorporated the seven most severely affected provinces (including Anhui, Jiangxi, Hubei, Hunan, Guangxi, Sichuan, and Guizhou, as shown in Supplementary Fig.  13a ) associated with their climatic conditions (hourly radiation intensity, temperature, wind speed, snowfall, and snow depth) during the snowstorm. Snowstorms often impose three typical effects on the near-zero power system (refer to Supplementary Note  5 for details).

The simulation of the 2021 sandstorm impact incorporated the eight most severely affected provinces (including Xinjiang, western Inner Mongolia, Gansu, Shanxi, Hebei, Beijing, Tianjin, and Ningxia, as shown in Supplementary Fig.  13a ) associated with their climatic conditions (hourly radiation intensity, temperature, and wind speed) during the sandstorm. Sandstorms often generate two typical effects on the near-zero power system (refer to Supplementary Note  5 for details).

The simulation of the 2022 drought impact incorporated the six most severely affected provinces (including Sichuan, Chongqing, Hubei, Hunan, Jiangxi, and Anhui, as shown in Supplementary Fig.  13b ) associated with their climatic conditions (hourly radiation intensity, temperature, wind speed, and drought level) during the drought. Droughts often impose three typical effects on the near-zero power system (refer to Supplementary Note  5 for details).

The simulation of the 2022 heat wave impact incorporated the 14 most severely affected provinces (including Hunan, Zhejiang, Chongqing, Jiangxi, Jiangsu, Anhui, Shanghai, Guangdong, Sichuan, Xinjiang, Henan, Hubei, Fujian, and Hainan, as shown in Supplementary Fig.  13b ) associated with their climatic conditions (hourly  radiation intensity, temperature, and wind speed) during the heat wave. Heat waves often exert three typical effects on the near-zero power system (please refer to Supplementary Note  5 for details).

Reliability and resilience of the power system

Both the reliability and resilience of the power system can be measured by power shortages. Referring to previous research 9 and national standards for the electricity supply (e.g., 99.9% for cities in China) 16 , reliability was defined as the ability of all generating units connected to the grid to meet the electricity demand in normal years (i.e., without extreme weather events), quantified as one minus the power shortage rate. Resilience mainly measures the power system ability to withstand power shortages and restore the electricity supply in a timely manner during extreme weather events 18 , 19 . Here, the power shortage degrees in affected areas during extreme weather periods were used to indicate the power system resilience, such as power shortage hours, highest power shortage rate, and total power shortage. Considering that zero-fossil fuel power generation and destroyed power transmission infrastructures can be recovered or rebuilt artificially during or after a climatic disaster (e.g., snowstorms), we assumed that the affected power system could gradually restore the normal electricity supply. A detailed demonstration of Lyapunov’s observability and controllability for the model is provided in Supplementary Note  6 , and the impact of renewable energy costs on the system cost is provided in Supplementary Note  7 .

CO 2 emissions accounting boundary

Here, we set the boundary to the direct CO 2 emission reduction related to all types of low-carbon technologies involved in the whole power system. Specifically, the indirect CO 2 emissions originating from wind power, solar PV, hydropower, and nuclear power were not considered, and only the remaining CO 2 emissions that could not be captured by CCUS retrofitts for coal-fired and gas-fired power plants were calculated (10% of the total emissions). As a result, we introduced negative emissions through the coal and biomass co-firing system coupled with CCUS to achieve complete net-zero emissions of the Chinese power system under the same emissions accounting framework.

Optimal matching model used for the coal and biomass co-firing system

Aiming at the coal and biomass co-firing system coupled with CCUS, we developed an optimal matching model to determine the optimal links between CCUS-qualified coal-fired power plants and surrounding biomass feedstocks 64 . This optimal matching model incorporates three biomass resources (agricultural residues, forest residues, and energy crops). The biomass data were spatialized into a 1 × 1 km 2 grid that could be individually and optimally selected by CCUS-qualified coal-fired power plants. The total amount of agricultural residues and forest residues were assumed to remain constant over time (as in 2015), while the total amount of future energy crops was estimated based on the distribution of suitable marginal land and the associated per-unit yield.

In this model, the matching mechanism was expressed by objective functions that minimized the total biomass transportation distance while maximizing the total biomass feedstocks constrained by the biomass availability (collection radius ≤50 km and maximum co-firing ratio ≤40%). This model introduces two important matching priorities for power plants with higher energy consumption and biomass feedstocks located closer to power plants, as well as the one-way matching rule (i.e., biomass feedstocks from each grid can only be matched with one power plant, while each power plant can receive biomass from multiple sourcing sites). The objective functions are defined in Eqs. ( 28 )–( 29 ).

where \(F{B}_{1}\) is the total biomass transportation distance (km), and \({m}_{p,b}\) is a binary variable describing if biomass grid b can be optimally linked to power plant p . If this is the case, \({m}_{p,b}=1\) , and \({m}_{p,b}=0\) otherwise. Moreover, \(D{B}_{p,b}\) is the straight-line distance between biomass grid b and power plant p (km), and \(F{B}_{2}\) denotes the total biomass feedstocks, both calculated from all surrounding biomass grids to the CCUS-qualified coal-fired power plants (tce). \(B{Q}_{p,b,k}\) is the amount of biomass k ( k  = 1, 2, 3, representing agricultural residues, forest residues, and energy crops, respectively) in biomass grid b linked to power plant p (t), and \({T}_{k}\) is the conversion coefficient of biomass k into standard coal, with values of 0.51, 0.57, and 0.52 tce/t, respectively.

The optimal matching model was constrained by the biomass availability associated with the maximum collection radius and the maximum co-firing ratio, as expressed in Eqs. ( 30 )–( 31 ).

where \(PGCC\) is the standard coal consumption per kWh of electricity generation in province (g/kWh), \(\theta\) is the maximum co-firing ratio of 40% by heat, and \({R}^{\max }\) is the maximum biomass collection radius of 50 km.

The total biomass feedstocks linked to a CCUS-qualified power plant were acquired by aggregating those from all potentially surrounding biomass grids, as defined in Eq. ( 32 ).

where \(AB{Q}_{p}\) denotes the total biomass feedstocks linked to power plant p (tce).

Data availability

Power supply and demand data generated in this study have been deposited in the Figshare platform [ https://doi.org/10.6084/m9.figshare.23614473 ], and can be obtained from [email protected] upon request. Corresponding data sources are listed in Supplementary Note  8 .

Code availability

Codes used in this study can be obtained from [email protected] upon request and are available at https://doi.org/10.6084/m9.figshare.23614473 .

World Energy Outlook 2021. https://www.iea.org/reports/world-energy-outlook-2021 (International Energy Agency, 2021).

Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. https://www.ipcc.ch/report/sixth-assessment-report-working-group-3/ (Cambridge University Press, 2022).

Renewable Power Generation Costs in 2020. https://www.irena.org/publications/2021/Jun/Renewable-Power-Costs-in-2020 (International Renewable Energy Agency, 2021).

Brown, P. R. & Botterud, A. The value of inter-regional coordination and transmission in decarbonizing the US electricity system. Joule 5 , 115–134 (2021).

Article   Google Scholar  

Hansen, K., Breyer, C. & Lund, H. Status and perspectives on 100% renewable energy systems. Energy 175 , 471–480 (2019).

Shaner, M. R., Davis, S. J., Lewis, N. S. & Caldeira, K. Geophysical constraints on the reliability of solar and wind power in the United States. Energy Environ. Sci. 11 , 914–925 (2018).

Tong, D. et al. Committed emissions from existing energy infrastructure jeopardize 1.5 degrees C climate target. Nature 572 , 373 (2019).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sepulveda, N. A., Jenkins, J. D., Edington, A., Mallapragada, D. S. & Lester, R. K. The design space for long-duration energy storage in decarbonized power systems. Nat. Energy 6 , 506–516 (2021).

Article   ADS   Google Scholar  

Tong, D. et al. Geophysical constraints on the reliability of solar and wind power worldwide. Nat. Commun. 12 , 6146 (2021).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Chen, X. et al. Pathway toward carbon-neutral electrical systems in China by mid-century with negative CO2 abatement costs informed by high-resolution modeling. Joule 5 , 2715–2741 (2021).

Article   CAS   Google Scholar  

Dowling, J. A. et al. Role of long-duration energy storage in variable renewable electricity systems. Joule 4 , 1907–1928 (2020).

MacDonald, A. E. et al. Future cost-competitive electricity systems and their impact on US CO2 emissions. Nat. Clim. Change 6 , 526–531 (2016).

Jenkins, J. D., Luke, M. & Thernstrom, S. Getting to zero carbon emissions in the electric power sector. Joule 2 , 2498–2510 (2018).

Alassi, A., Bañales, S., Ellabban, O., Adam, G. & MacIver, C. HVDC transmission: technology review, market trends and future outlook. Renew. Sustain. Energy Rev. 112 , 530–554 (2019).

Bertsch, V., Hyland, M. & Mahony, M. What drives people’s opinions of electricity infrastructure? Empirical evidence from Ireland. Energy Policy 106 , 472–497 (2017).

“Ten Commitments” of Power Supply Service of State Grid Corporation of China. http://www.sc.sgcc.com.cn/html/files/2022-03/25/20220325152015177346621.pdf (State Grid Corporation of China, 2022).

Perera, A. T. D., Nik, V. M., Chen, D., Scartezzini, J.-L. & Hong, T. Quantifying the impacts of climate change and extreme climate events on energy systems. Nat. Energy 5 , 150–159 (2020).

Bie, Z., Lin, Y., Li, G. & Li, F. Battling the extreme: a study on the power system resilience. Proc. IEEE 105 , 1253–1266 (2017).

Feng, K., Ouyang, M. & Lin, N. Tropical cyclone-blackout-heatwave compound hazard resilience in a changing climate. Nat. Commun. 13 , 4421 (2022).

Kraan, O., Kramer, G. J., Haigh, M. & Laurens, C. An energy transition that relies only on technology leads to a bet on solar fuels. Joule 3 , 2286–2290 (2019).

Heuberger, C. F., Staffell, I., Shah, N. & Mac Dowell, N. Quantifying the value of CCS for the future electricity system. Energy Environ. Sci. 9 , 2497–2510 (2016).

Sepulveda, N. A., Jenkins, J. D., de Sisternes, F. J. & Lester, R. K. The role of firm low-carbon electricity resources in deep decarbonization of power generation. Joule 2 , 2403–2420 (2018).

Byers, E. A., Coxon, G., Freer, J. & Hall, J. W. Drought and climate change impacts on cooling water shortages and electricity prices in Great Britain. Nat. Commun. 11 , 2239 (2020).

Global Status of CCS Report 2021. https://www.globalccsinstitute.com/ (Global CCS Institute, 2021).

The Global Status of CCS 2018. https://indd.adobe.com/view/2dab1be7-edd0-447d-b020-06242ea2cf3b (Global CCS Institute, 2018).

Jenkins, S., Mitchell-Larson, E., Ives, M. C., Haszeldine, S. & Allen, M. Upstream decarbonization through a carbon takeback obligation: an affordable backstop climate policy. Joule 5 , 2777–2796 (2021).

Pehl, M. et al. Understanding future emissions from low-carbon power systems by integration of life-cycle assessment and integrated energy modelling. Nat. Energy 2 , 939–945 (2017).

Article   ADS   CAS   Google Scholar  

Singh, S. P. et al. Large-scale affordable CO2 capture is possible by 2030. Joule 3 , 2154–2164 (2019).

Lu, Y. S. Y., Cohen, F., Smith, S. M. & Pfeiffer, A. Plant conversions and abatement technologies cannot prevent stranding of power plant assets in 2 degrees C scenarios. Nat. Commun. 13 , 806 (2022).

Wang H. et al. Early transformation of the Chinese power sector to avoid additional coal lock-in. Environ. Res. Lett. 15 , 024007 (2020).

Patrizio, P. et al. Reducing US coal emissions can boost employment. Joule 2 , 2633–2648 (2018).

CCUS in Clean Energy Transitions. https://www.iea.org/reports/ccus-in-clean-energy-transitions (International Energy Agency, 2020).

Kaya, A., Csala, D. & Sgouridis, S. Constant elasticity of substitution functions for energy modeling in general equilibrium integrated assessment models: a critical review and recommendations. Clim. Change 145 , 27–40 (2017).

Hohmeyer, O. H. & Bohm, S. Trends toward 100% renewable electricity supply in Germany and Europe: a paradigm shift in energy policies. Wiley Interdiscip. Rev.-Energy Environ. 4 , 74–97 (2015).

Bogdanov, D. et al. Radical transformation pathway towards sustainable electricity via evolutionary steps. Nat. Commun. 10 , 1077 (2019).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Rogelj, J. et al. Energy system transformations for limiting end-of-century warming to below 1.5 °C. Nat. Clim. Change 5 , 519–527 (2015).

Jacobson, M. Z. et al. Impacts of green new deal energy plans on grid stability, costs, jobs, health, and climate in 143 countries. One Earth 1 , 449–463 (2019).

Bistline, J. E. T. & Young, D. T. The role of natural gas in reaching net-zero emissions in the electric sector. Nat. Commun. 13 , 4743 (2022).

Riera, J. A., Lima, R. M., Hoteit, I. & Knio, O. Simulated co-optimization of renewable energy and desalination systems in Neom, Saudi Arabia. Nat. Commun. 13 , 3514 (2022).

Zhuo, Z. et al. Cost increase in the electricity supply to achieve carbon neutrality in China. Nat. Commun. 13 , 3172 (2022).

Bennett, J. A. et al. Extending energy system modelling to include extreme weather risks and application to hurricane events in Puerto Rico. Nat. Energy 6 , 240–249 (2021).

Zeyringer, M., Price, J., Fais, B., Li, P.-H. & Sharp, E. Designing low-carbon power systems for Great Britain in 2050 that are robust to the spatiotemporal and inter-annual variability of weather. Nat. Energy 3 , 395–403 (2018).

Sgouridis, S., Carbajales-Dale, M., Csala, D., Chiesa, M. & Bardi, U. Comparative net energy analysis of renewable electricity and carbon capture and storage. Nat. Energy 4 , 456–465 (2019).

Lu, T. et al. India’s potential for integrating solar and on- and offshore wind power into its energy system. Nat. Commun. 11 , 4750 (2020).

An Energy Sector Roadmap to Carbon Neutrality in China. https://www.iea.org/reports/an-energy-sector-roadmap-to-carbon-neutrality-in-china (International Energy Agency, 2021).

Energy Statistics Data Browser. https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=CO2%20emissions&indicator=CO2BySector (International Energy Agency, 2019).

World Bank Database. https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?lang=en%2Cen&name_desc=true&year_high_desc=true (International Bank forReconstruction and Development, 2021).

Global Status of CCS Report: 2020. https://www.globalccsinstitute.com/resources/publications-reports-research/global-status-of-ccs-report-2020/ (Global CCS Institute, 2020).

Tang, H., Zhang, S. & Chen, W. Assessing representative CCUS layouts for China’s power sector toward carbon neutrality. Environ. Sci. Technol. 55 , 11225–11235 (2021).

Wei, N. et al. Decarbonizing the coal-fired power sector in china via carbon capture, geological utilization, and storage technology. Environ. Sci. Technol. 55 , 13164–13173 (2021).

CAS   PubMed   Google Scholar  

Wei, Y.-M. et al. A proposed global layout of carbon capture and storage in line with a 2 °C climate target. Nat. Clim. Change 11 , 112–118 (2021).

Diagram of the State Grid’s UHV projects under construction and in operation. http://www.sgcc.com.cn/html/sgcc_main/index.shtml?WxUg5ztDmi=1670077508780#here (State Grid Corporation of China, 2022).

China power industry development report 2021. https://cec.org.cn/detail/index.html?3-298424 (China Electricity Council, 2021).

He, G. et al. Rapid cost decrease of renewables and storage accelerates the decarbonization of China’s power system. Nat. Commun. 11 , 2486 (2020).

Lu, Y., Cohen, F., Smith, S. M. & Pfeiffer, A. Plant conversions and abatement technologies cannot prevent stranding of power plant assets in 2 °C scenarios. Nat. Commun. 13 , 806 (2022).

Pfeiffer A., Hepburn C., Vogt-Schilb A., Caldecott B. Committed emissions from existing and planned power plants and asset stranding required to meet the Paris Agreement. Environ. Res. Lett. 13 , 5 (2018).

Maia-Silva, D., Kumar, R. & Nateghi, R. The critical role of humidity in modeling summer electricity demand across the United States. Nat. Commun. 11 , 1686 (2020).

Yalew, S. G. et al. Impacts of climate change on energy systems in global and regional scenarios. Nat. Energy 5 , 794–802 (2020).

Xing X. F. et al. Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China. Nat. Commun. 12 , 3159 (2021).

Agbor, E., Zhang, X. & Kumar, A. A review of biomass co-firing in North America. Renew. Sustain. Energy Rev. 40 , 930–943 (2014).

Basu, P., Butler, J. & Leon, M. A. Biomass co-firing options on the emission reduction and electricity generation costs in coal-fired power plants. Renew. Energy 36 , 282–288 (2011).

Fan J.-L. et al. Carbon reduction potential of China’s coal-fired power plants based on a CCUS source-sink matching model . Resour. Conserv. Recycl. 168 , 105320 (2021).

He, G. et al. SWITCH-China: a systems approach to decarbonizing China’s power system. Environ. Sci. Technol. 50 , 5467–5473 (2016).

Article   ADS   CAS   PubMed   Google Scholar  

Fan J.-L. et al. Co-firing plants with retrofitted carbon capture and storage for power-sector emissions mitigation. Nat. Clim. Change 13 , 1–9 (2023).

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Acknowledgements

The original reserach work was supported by the National Natural Science Foundation of China (No. 72174196 and No. 71874193 to J.-L.F.), Open Fund of State Key Laboratory of Coal Resources and Safe Mining (No. SKLCRSM21KFA05 to J.-L.F.), and the Fundamental Research Funds for the Central Universities (No. 2022JCCXNY02 to J.-L.F.). We also thank the contributions from Wenlong Su, Wenlong Zhou, Xinmeng Guan, Yujiao Xian, Jiayu Li, and Zixia Ding on the data collection and analysis discussion.

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X.Z. and J.L.F. designed the research. Z.L., J.L.F., and X.H. performed the integrated model simulation and data compiling. J.L.F. wrote the article with major contributions provided by Z.L., X.Z., K.L., X.L., J.W., K.H., and B.S.

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Fan, JL., Li, Z., Huang, X. et al. A net-zero emissions strategy for China’s power sector using carbon-capture utilization and storage. Nat Commun 14 , 5972 (2023). https://doi.org/10.1038/s41467-023-41548-4

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The dynamic change of energy supply and demand structure within China: a perspective from the national value chain

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The complexity of the national value chain (NVC) has significantly impacted the level of energy consumption embodied in domestic trade. This paper aims to measure embodied energy and its spatial transfer between provinces and determine the dynamic change in the energy consumption supply and demand structure within China. This paper applies the multiregional input–output model (MRIO) to establish three accounting frameworks from forward-linkage, backward-linkage, and trade in value-added (TiVA) perspectives to comprehensively track energy consumption transfer along the NVC during the period 2007 to 2017. The results reveal that provinces acting as net energy importers are primarily in developed and coastal regions (Beijing, Shanghai, Jiangsu, Zhejiang, and Guangdong). The net energy exporters are concentrated in Hebei, Shanxi, Inner Mongolia, Liaoning, Heilongjiang, Xinjiang, and other energy-intensive provinces. By and large, China’s interprovincial energy transfer pattern gradually evolved from one of “shifting from inland provinces in the northwest and central regions to developed provinces in the east” to one of “shifting from the northwest, northeast, and central provinces to southwest and southeast provinces with the Yangtze River Delta and central provinces as pivots” during the period 2007–2017. By comparing the embodied energy transfer measured in three accounting frameworks, we find that this embodied energy transfer tends to be exaggerated from the forward-linkage and backward-linkage perspectives due to multiple cross-border trading of intermediates and double-counting. These findings indicate that complex domestic businesses, especially intermediates, exert a significant impact on energy transfer, resulting in more complicated energy flows. This article has significance for better understanding the energy transfer pattern and evolution process.

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The word “province” in this paper refers to 22 provinces, 4 municipalities, and 4 autonomous regions in China’s MRIO tables mentioned subsequently

An Q, An HZ, Wang L, Gao XY, Lv N (2015) Analysis of embodied exergy flow between Chinese industries based on network theory. Ecol Model 318:26–35. https://doi.org/10.1016/j.ecolmodel.2015.01.020

Article   Google Scholar  

Andrew RM, Peters GP (2013) A multi-region input-output table based on the Global Trade Analysis Project Database (GTAP-MRIO). Econ Syst Res 25:99–121. https://doi.org/10.1080/09535314.2012.761953

Arto I, Dietzenbacher E (2014) Drivers of the growth in global greenhouse gas emissions. Environ Sci Technol 48:5388–5394. https://doi.org/10.1021/es5005347

Article   CAS   Google Scholar  

Chen B, Li JS, Wu XF, Han MY, Zeng L, Li Z, Chen GQ (2018) Global energy flows embodied in international trade: a combination of environmentally extended input-output analysis and complex network analysis. Appl Energy 210:98–107. https://doi.org/10.1016/j.apenergy.2017.10.113

Chen GQ, Wu XF (2017) Energy overview for globalized world economy: source, supply chain and sink. Renew Sust Energ Rev 69:735–749. https://doi.org/10.1016/j.rser.2016.11.151

Chen J, Zhou W, Yang H (2019) Is embodied energy a better starting point for solving energy security issues?—Based on an overview of embodied energy-related research. Sustainability 11:4260. https://doi.org/10.3390/su11164260

Chen ZM, Chen GQ (2011) An overview of energy consumption of the globalized world economy. Energy Policy 39:5920–5928. https://doi.org/10.1016/j.enpol.2011.06.046

Costanza R (1980) Embodied energy and economic valuation. Science 210:1219–1224. https://doi.org/10.1126/science.210.4475.1219

CSY (2022) China statistical yearbook 2021. China statistics press, Beijing

Google Scholar  

Cui LB, Peng P, Zhu L (2015) Embodied energy, export policy adjustment and China’s sustainable development: a multi-regional input-output analysis. Energy 82:457–467. https://doi.org/10.1016/j.energy.2015.01.056

Daudin G, Rifflart C, Schweisguth D (2011) Who produces for whom in the world economy? Can J Econ 44:1403–1437. https://doi.org/10.1111/j.1540-5982.2011.01679.x

Davis SJ, Caldeira K (2010) Consumption-based accounting of CO2 emissions. Proc Natl Acad Sci U S A 107:5687–5692. https://doi.org/10.1073/pnas.0906974107

Gao D, Li Y, Yang QY (2021) Can pollution charges reform promote industrial SO2 emissions reduction?—Evidence from 189 China’s cities. Energy Environ 32:96–112. https://doi.org/10.1177/0958305x20919410

Gao D, Li G, Li Y, Gao KX (2022a) Does FDI improve green total factor energy efficiency under heterogeneous environmental regulation? Evidence from China. Environ Sci Pollut Res 29:25665–25678. https://doi.org/10.1007/s11356-021-17771-1

Gao D, Li G, Yu JY (2022b) Does digitization improve green total factor energy efficiency? Evidence from Chinese 213 cities. Energy 247:12. https://doi.org/10.1016/j.energy.2022.123395

Girod B, van Vuuren DP, Hertwich EG (2014) Climate policy through changing consumption choices: options and obstacles for reducing greenhouse gas emissions. Glob Environ Change-Human Policy Dimens 25:5–15. https://doi.org/10.1016/j.gloenvcha.2014.01.004

Guo CX, Hu YM (2019) Research on the cross-border transfer of embodied energy under global production division system, China population,resources and environment. 29:10 (in Chinese)

Guo S, Han MY, Yang YP, Di HN (2020) Embodied energy flows in China’s economic zones: Jing-Jin-Ji, Yangtze-River-Delta and Pearl-River-Delta. J Clean Prod 268:8. https://doi.org/10.1016/j.jclepro.2020.121710

Guo S, Han MY, Yang YP (2021) Multiregional embodied energy flows and energy efficiency redundancy in China. Resources Sci 43:12 (in Chinese)

Han Z, Wang G, Economics SO (2019) Estimation of embodied energy and carbon emissions in Sino-U.S.trade based on multi-region input-output model. Clim Change Res 15:416–426 (in Chinese). https://doi.org/10.12006/j.issn.1673-1719.2018.154

Hasegawa R, Kagawa S, Tsukui M (2015) Carbon footprint analysis through constructing a multi-region input–output table: a case study of Japan. J Econ Struct 4:5. https://doi.org/10.1186/s40008-015-0015-6

HERTWICH EGG, Peters GP (2009) Carbon footprint of nations: a global, trade-linked analysis. Environ Sci Technol 43:6414–6420. https://doi.org/10.1021/es803496a

Hong JK, Tang MH, Wu ZZ, Miao Z, Shen GQP (2019) The evolution of patterns within embodied energy flows in the Chinese economy: a multi-regional-based complex network approach. Sustain Cities Soc 47:101500. https://doi.org/10.1016/j.scs.2019.101500

Huang W, Li Y, Li H (2021) Embodied energy in China: drivers and inequality at a regional level. Environ Sci Pollut Res Int 28:64488–64502. https://doi.org/10.1007/s11356-021-15328-w

Jakob M, Steckel JC, Edenhofer O (2014) Consumption-versus production-based emission policies. Ann Rev Resour Econ 6:297–318. https://doi.org/10.1146/annurev-resource-100913-012342

Johnson RC, Noguera G (2012) Accounting for intermediates: production sharing and trade in value added. J Int Econ 86:224–236. https://doi.org/10.1016/j.jinteco.2011.10.003

Koopman R, Wang Z, Wei SJ (2014) Tracing value-added and double counting in gross exports. Am Econ Rev 104:459–494. https://doi.org/10.1257/aer.104.2.459

Leontief WW (1936) Quantitative input and output relations in the economic systems of the United States. Rev Econ Stat 18:105–125. https://doi.org/10.2307/1927837

Li F (2016) Division of China’s national value chain on the perspective of value-added——based on the modified regional input-output model. China Ind Econ:52–67 (in Chinese). https://doi.org/10.19581/j.cnki.ciejournal.2016.03.005

Li F (2020) Chinese provincial regionals’ role under the dual value chain——a comprehensive theoretical analysis framework. China Ind Econ:136–154 (in Chinese). https://doi.org/10.19581/j.cnki.ciejournal.2020.01.009

Li FY, Liu WD, Gong PP, Management SO (2015) Regional differences and inter-regional linkage of industrial energy consumption in China. Sci Geogr Sin (in Chinese). https://doi.org/10.13249/j.cnki.sgs.2015.01.005

Li YM, Niu MM, Zhang HL (2019) Comparison of economic benefits and embodied carbon emissions of intraregional value-added trade in the Beijing-Tianjin-Hebei region. Resources Sci 41:1619–1629 (in Chinese)

Liu B, Zhang L, Liu CL, Wang DD, Sun JD, Luther M, Xu YQ (2019) Measuring energy poverty based on energy embodied in exports of vertical specialisation trade in the construction sector. Energy Build 196:157–168. https://doi.org/10.1016/j.enbuild.2019.05.022

Liu B, Zhang L, Sun JD, Wang DD, Liu CL, Luther M, Xu YQ (2020a) Composition of energy outflows embodied in the gross exports of the construction sector. J Clean Prod 248:119296.1–119296.14. https://doi.org/10.1016/j.jclepro.2019.119296

Liu B, Zhang L, Sun JD, Wang DD, Liu CL, Luther M, Xu YQ (2020b) Analysis and comparison of embodied energies in gross exports of the construction sector by means of their value-added origins. Energy 191:116546. https://doi.org/10.1016/j.energy.2019.116546

Liu Z, Davis SJ, Feng K, Hubacek K, Liang S, Anadon LD, Chen B, Liu JR, Yan JY, Guan DB (2016) Targeted opportunities to address the climate-trade dilemma in China. Nat Clim Chang 6:201-+. https://doi.org/10.1038/nclimate2800

Liu ZM, Huang XY, Li MY (2021) Accounting and international comparison of energy embodied in international trade of intermediate products. J Manag World 37:109–128 (in Chinese)

CAS   Google Scholar  

Meng B, Peters GP, Wang Z, Li M (2018) Tracing CO2 emissions in global value chains. Energy Econ 73:24–42. https://doi.org/10.1016/j.eneco.2018.05.013

Mi ZF, Meng J, Guan DB, Shan YL, Song ML, Wei YM, Liu Z, Hubacek K (2017) Chinese CO2 emission flows have reversed since the global financial crisis. Nat Commun 8:1–10. https://doi.org/10.1038/s41467-017-01820-w

Pan A (2017) The effect of GVC division on carbon emission embodied in China’s foreign trade. Int Econ Trade Res 33:14–26 (in Chinese). https://doi.org/10.13687/j.cnki.gjjmts.2017.03.002

Pan A (2018) Embodied carbon in China-US trade from perspective of global value chain. Stat Res 35:53–64. https://doi.org/10.19343/j.cnki.11-1302/c.2018.01.006

Peters GP (2008) From production-based to consumption-based national emission inventories. Ecol Econ 65:13–23. https://doi.org/10.1016/j.ecolecon.2007.10.014

Peters GP, Minx JC, Weber CL, Edenhofer O (2011) Growth in emission transfers via international trade from 1990 to 2008. Proc Natl Acad Sci 108:8903–8908. https://doi.org/10.1073/pnas.1006388108

Shi JL, Li HJ, Guan JH, Sun XQ, Guan Q, Liu XJ (2017) Evolutionary features of global embodied energy flow between sectors: a complex network approach. Energy 140:395–405. https://doi.org/10.1016/j.energy.2017.08.124

Shigeto S, Yamagata Y, Ii R, Hidaka M, Horio M (2012) An easily traceable scenario for 80% CO2 emission reduction in Japan through the final consumption-based CO2 emission approach: a case study of Kyoto-city. Appl Energy 90:201–205. https://doi.org/10.1016/j.apenergy.2011.03.049

Su B, Thomson E (2016) China’s carbon emissions embodied in (normal and processing) exports and their driving forces, 2006-2012. Energy Econ 59:414–422. https://doi.org/10.1016/j.eneco.2016.09.006

Sun T, Zhao TY (2014) Measurement and trends of carbon emission in China’s energy consumption. J Audit Econ 2:104–111 (in Chinese).

Sun XD, Li JS, Qiao H, Zhang B (2017) Energy implications of China’s regional development: new insights from multi-regional input-output analysis. Appl Energy 196:118–131. https://doi.org/10.1016/j.apenergy.2016.12.088

Sun XQ, An HZ, Gao XY, Jia XL, Liu XJ (2016) Indirect energy flow between industrial sectors in China: a complex network approach. Energy 94:195–205. https://doi.org/10.1016/j.energy.2015.10.102

Tang MH, Hong JK, Liu GW, Shen GQ (2019) Exploring energy flows embodied in China’s economy from the regional and sectoral perspectives via combination of multi-regional input-output analysis and a complex network approach. Energy 170:1191–1201. https://doi.org/10.1016/j.energy.2018.12.164

Timmer MP, Erumban AA, Los B, Stehrer R, De Vries GJ (2014) Slicing up global value chains. J Econ Perspect 28:99–118. https://doi.org/10.1257/jep.28.2.99

Wang AJ, Meng B, Feng ZX, Liu Y (2020) Research on China’s regional carbon emission transfers from the perspective of value added trade. J Xi'an Jiaotong Univ (Soc Sci) 40:85–94 (in Chinese). https://doi.org/10.15896/j.xjtuskxb.202002010

Wang F, Gao CH (2020) Measurement, decomposition, and cross border transfer of embodied energy of China’s industrial sectors: based on the CRIO model. Inquiry into Economic Issues 9:1–11 (in Chinese).

Wang Z, Wei SJ, Zhu KF (2015) Gross trade accounting method: official trade statistics and measurement of the global value chain. China. Soc Sci 237:108-127+205-206 (in Chinese)

Wei T, Peng SJ (2017) Embodied energy and carbon emissions transferred in international trade using a MRIO model. Resources Sci 39:94–104 (in Chinese). https://doi.org/10.18402/resci.2017.01.10

Xu GX, Yang ZJ (2022) The mechanism and effects of national smart city pilots in China on environmental pollution: empirical evidence based on a DID model. Environ Sci Pollut Res 29:41804–41819. https://doi.org/10.1007/s11356-021-18003-2

Yu LL, Peng SJ (2018) Research on carbon emission transfer effect of Chinese regions in the global value chains. Stat Res 35:14 (in Chinese)

Zhang B, Chen ZM, Xia XH, Xu XY, Chen YB (2013) The impact of domestic trade on China’s regional energy uses: a multi-regional input-output modeling. Energy Policy 63:1169–1181. https://doi.org/10.1016/j.enpol.2013.08.062

Zhang B, Qiao H, Chen B (2015) Embodied energy uses by China’s four municipalities: a study based on multi-regional input-output model. Ecol Model 318:138–149. https://doi.org/10.1016/j.ecolmodel.2014.10.007

Zhang BY, Bai SK, Ning YD (2021) Embodied energy in export flows along global value chain: a case study of China’s export trade. Front Energy Res 9:25. https://doi.org/10.3389/fenrg.2021.649163

Zhao N, Xu LX, Malik A, Song XG, Wang YF (2018) Inter-provincial trade driving energy consumption in China. Resour Conserv Recycl 134:329–335. https://doi.org/10.1016/j.resconrec.2017.09.009

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This work was financially supported by the Fundamental Research Funds for the Central Universities (CXJJ-2021-358).

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The following section explains how we derive energy consumption accounting frameworks based on Leontief’s work from forward-linkage, backward-linkage, and TiVA perspectives.

Forward-linkage-based accounting framework

Consider an economy with G regions and N sectors as a rule. The general interregion input–output model in Appendix Table 1 depicts its input–output structure.

According to the basic input–output model, the N × N input matrix X ij expresses intermediate use in region j of goods produced in region i ; similarly, the N × N matrix A ij denotes the direct input coefficient of region i to region j, and each entry is calculated by the corresponding intermediate consumption to divide gross input. Y ij is an N × 1 vector indicating final demands of region j that are produced in region i ; Ex i denotes an N × 1 vector indicating exports of N sectors in region i ; M i is a 1 × N vector of imports in region i ; V i denotes a 1 × N vector giving value-added of N sectors in region i . Then, we obtain the following equation according to the row relationship in the IO model:

According to Eq. ( 6 ), we can decompose the total outputs in region s based on the forward-linkage perspective as follows:

All the total outputs in region s can be decomposed into intermediate use and final use using Eq. ( 6 ):

where \({A}^{ss}{X}^s+{\sum}_{t\ne s}^G{A}^{s\mathrm{t}}{X}^t\) denotes intermediate flows, and \({Y}^{ss}+{\sum}_{t\ne s}^G{Y}^{st}+E{x}^s\) presents the overall final goods produced in region s denoted by Y s . From the forward-linkage perspective, A ss X s  +  Y ss represents the portion of products produced and used in region s ; \({\sum}_{t\ne s}^G{A}^{s\mathrm{t}}{X}^t+{\sum}_{t\ne s}^G{Y}^{st}\) describes the portion of products produced in region s and used by other regions in the economic system.

By rearranging Eq. ( 6 ), we have:

where the Leontief inverse matrix \({B}^{ij}=\left[{b}_{pq}^{ij}\right],,\left(p,q=1,2,\dots, N\right)\) is also called the total requirements matrix, in which \({b}_{pq}^{ij}\) denotes the total requirement in region j sector q to produce one-unit final products required from region i sector p . A similar decomposition is possible for the total output in region s:

In particular, B ss in the equation above is not equivalent to the local Leontief inverse matrix L ss . According to the work of Wang et al. (2018), \({L}^{ss}\sum_{r\ne s}^G{A}^{sr}{B}^{rs}={B}^{ss}-{L}^{ss}\) . By further decomposing the first and third terms in Eq. ( 9 ), we obtain the following complete decomposition of the outputs of regions s along the NVC:

Denote the vector \({F}^s={\left({f}_1^s,{f}_2^s,\dots, {f}_N^s\right)}^{\prime }\) as the direct energy consumption intensity of N sectors in region s , and each element is defined by \({f}_j^s= emi{s}_j^s/{X}_j^s\) . \(emi{s}_j^s\) is the total energy consumption of sector j in region s. \({X}_j^s\) is the total output of sector j in region s. By combining the direct energy consumption intensity with Eq. ( 10 ), we can decompose the energy consumption generated in region s from a forward-linkage perspective in the following way:

Equation ( 11 ) has 9 terms, each representing the energy consumption produced in region s to meet various downstream demands along the NVC.

Term (1): energy consumption in final goods that are produced and consumed locally in region s without any participation in the NVC and GVC.

Term (2): energy consumption in the production of intermediate goods in region s , which are then returned to home region s after the multistage NVC to produce locally consumed final goods.

Term (3): energy consumption in the production of intermediate goods in region s , which are then used by partner region r to produce final goods returned to home region s .

Term (4): energy consumption in the production of final goods in region s , which are then consumed in partner region r .

Term (5): energy consumption in the production of intermediate goods in region s , which is used by partner region r to produce final goods consumed there.

Term (6): energy consumption in the production of intermediate goods in region s , which is used by partner region r to produce final goods consumed by third regions.

Term (7): energy consumption in the production of goods in region s , which are directly exported to regions outside the economic system without any participation in the NVC.

Term (8): energy consumption in the production of intermediate goods in region s , which are then returned to home region s after the multistage NVC to produce exports to regions outside the economic system.

Term (9): energy consumption in the production of intermediate goods in region s , which is used by partner region r to produce exports to regions outside the economic system.

Thus, we can trace energy consumption transfers from upstream to downstream by applying the forward-linkage accounting framework presented in Eq. ( 11 ). Note that each term on the right side of Eq. ( 11 ) sequentially corresponds to an energy transfer route described in the context and figures based on the forward-linkage perspective.

Backward-linkage-based accounting framework

Then, we explain how we decompose energy consumption from a backward-linkage perspective. According to the Armington model, there are two assumptions that imports cannot completely replace locally produced goods and that a complete value chain encompasses separate stages of production in different regions and industries. By rearranging Eq. ( 8 ), we can decompose the inputs to produce final goods from downstream to upstream:

where \({Y}^s={\left({y}_1^s,{y}_2^s,\dots, {y}_N^s\right)}^{\prime }\) is the vector of the final goods produced in region s , and \({\hat{Y}}^j\left(j=1,2,\dots, G\right)\) is the corresponding diagonal matrix. The j- th column on the right side of Eq. ( 12 ) gives the input demands at various stages for the production of final goods in region j .

Then, we obtain the following decomposition of the energy consumption required for final goods production:

Similarly, the j th column represents energy consumption in upstream regions and sectors that are required to produce final goods in region j . For example, energy consumption embodied in intermediates from region i sector p , which are needed by region j sector q to produce final goods, is \({f}_p^i{b}_{pq}^{ij}{Y}_q^j\) . Additionally, the more nonzero elements there are in each column, the longer the production value chain associated with a particular final product. Then, the total energy consumption induced by the production of final goods in region s can be decomposed as follows:

Equation ( 14 ) indicates where the total energy consumption embodied in the final products produced in region s is generated.

Term (1): energy consumption generated by local sectors to produce final goods in region s .

Term (2): energy consumption generated by other regions and sectors in NVCs to produce final goods in region s .

TiVA-based accounting framework

To obtain the TiVA-based accounting framework, we begin by extending the gross trade accounting method (Wang et al. 2015 ) to the NVC level and obtaining a comprehensive view of the economic system’s TiVA structure. Then, we create a unified TiVA-based accounting framework by combining energy consumption intensity with the TiVA structure.

For an economy with G regions and N sectors, Eq. ( 7 ) in A.1. shows the total outputs in region j , i.e., \({X}^j={A}^{jj}{X}^j+{Y}^{jj}+{\sum}_{t\ne j}^G{A}^{jt}{X}^t+{\sum}_{t\ne j}^G{Y}^{jt}+E{x}^j\) , rearranging it as:

The interregional trade in the NVC for region j is defined as \({T}^j={\sum}_{t\ne j}^G{A}^{jt}{X}^t+{\sum}_{t\ne j}^G{Y}^{jt},\left(j=1,2,\dots, G\right)\) , and the matrix form of Eq. ( 15 ) is as follows:

Then, the intermediate trade from regions i and j (Z ij ) can be expressed as Eq. ( 17 ) using Eq. ( 15 ) and can also be written as Eq. ( 18 ) using \({X}^j={\sum}_{r=1}^G{B}^{jr}{\sum}_{t=1}^G{Y}^{rt}+{\sum}_{r=1}^G{B}^{jr}E{x}^r\) induced by Eq. (8):

We denote the 1 × GN vector  \(V={\left({v}_1^1,\dots, {v}_N^1,\dots, {v}_1^G,\dots, {v}_N^G\right)}^{\prime }\) as the direct value-added coefficient, and each element means the direct value-added coefficient of region s sector i defined by \({v}_i^s=v{a}_i^s/{X}_i^s\) . \(v{a}_i^s\) is the value added of region s sector i . Then, we obtain the total value-added coefficient matrix VB as follows:

Likewise, the import input coefficient of region s sector i is defined by \({m}_i^s= im{p}_i^s/{X}_i^s\) ; \(im{p}_i^s\) is the imports of region s sector i , which are used as intermediate inputs to produce outputs. The total import inputs coefficient matrix MB can be derived as follows:

Since final goods can be entirely divided into value-added of all regions and sectors both domestically and internationally, which means VB  +  MB  =  u , ( u  = [1, 1, …, 1] 1 ×  GN ). This decomposition is based on the source of value-added in trade and the backward linkages between industries. For region i , we have:

Thus, we can decompose the interregional trade from region i to region j into 26 terms using Eqs. ( 17 ) and ( 18 ) with ( 21 ) as follows:

which includes double counting terms ((11), (12), (17), and (23)) and import-related terms ((13), (14), (19), (20), (25), and (26)). Multiple cross-border transactions of intermediate goods result in double counting; however, they do not constitute GDP or final demand in any country and do not generate new energy consumption. As a result, the energy consumption accounting framework must remove these double counting terms. Additionally, omitting these import-related terms is required for calculating energy consumption, as the goal of this research is to account for energy consumed during domestic production. It is extremely difficult to determine the aggregate imported energy consumption intensity of other regions beyond the 30-province MRIO table.

V, defined as the direct value-added coefficient matrix, aids in tracing the sources and destinations of value-added in interregional trade. Similarly, we could use F, the direct energy consumption intensity defined as energy consumed by one-unit output, to access the following decomposition of energy consumption embodied in the exports from region i to region j with no double terms.

The TiVA-based accounting method presented in Eq. ( 23 ) could be used to trace the sources and final destinations of energy consumption embodied in the production of regional exports along with the NVC.

Term (1): energy consumption from home region i in the production of final goods absorbed by partner region j .

Term (2): energy consumption from home region i in the production of intermediate goods used by partner region j to produce final goods absorbed there.

Term (3): energy consumption from home region i in the production of intermediate goods used by partner region j to produce final goods absorbed by third regions.

Term (4): energy consumption from home region i in the production of intermediate goods used by partner region j to produce final goods returned and absorbed by region i .

Term (5): energy consumption from home region i in the production of intermediate goods used by partner region j to produce exports or intermediate goods used by third regions to produce exports.

Term (6): energy consumption from partner region j provided to region i ’s production of final goods absorbed by region j .

Term (7): energy consumption from partner region j provided to region i ’s production of intermediate goods used by region j to produce final goods absorbed there.

Term (8): energy consumption from partner region j provided to region i ’s production of intermediate goods used by region j to produce exports.

Term (9): energy consumption from third regions provided to region i ’s production of final goods absorbed by region j .

Term (10): energy consumption from third regions provided to region i ’s production of intermediate goods used by region j to produce final products absorbed there.

Term (11): energy consumption from third regions provided to region i ’s production of intermediate goods used by region j to produce exports.

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Fan, N., Ji, H. The dynamic change of energy supply and demand structure within China: a perspective from the national value chain. Environ Sci Pollut Res 30 , 11873–11892 (2023). https://doi.org/10.1007/s11356-022-22676-8

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Home » Blog » How China is Winning the Race for Clean Energy Technology

How China is Winning the Race for Clean Energy Technology

For the past two decades, a clean energy revolution has been quietly taking place across the globe. More recently, a heated global race has begun with China at the head of the pack.

Judging from the emissions data, you wouldn’t think China is ahead. China’s carbon emissions have yet to peak, while the United States and the European Union have drastically lowered their carbon emissions over the last decade. The UK is now at 44% below its 1990 levels and plans to reduce annual carbon emissions to 70% below 1990 levels by 2045. Having peaked its carbon emissions in 2007, the United States has reduced its carbon emissions to 2% above its 1990 levels. China plans to peak its emissions by 2030. But recent investments in fossil fuel have cast doubt on that scenario.

At the same time, however, China has been making massive investments in energy transition. “China saw an economic opportunity in low-carbon industries,” according to Kelly Sims Gallagher, Director at the Tufts University Center for International Environment and Resource Policy, “and they formed industrial policy.” While Europe was an early mover in clean energy, China has harnessed its competitive advantage in policy consistency and lower-cost capital to lead the market in solar, wind, and EV technology exports.  With the passage of the CHIPS, Infrastructure, and Inflation reduction bills in 2022, the United States has finally significantly accelerated clean energy investment. But is it too late for the Americans to catch up? At a recent Critical Issues Confronting China talk hosted by the Fairbank Center for Chinese Studies, Gallagher gave an update on the current state of the global race for Leadership in low-carbon tech and provided her outlook on the future of clean energy.

 Five key takeaways:

China’s clean energy innovation was driven by economic opportunism.

By 2009, China had made strong political commitments to renewable energy development and there was a noted government acceptance of climate science. But it was rather recognition of the immense economic potential in the green energy innovation that drove China past its tipping point. In 2009, China was already on track to pass the US as the world’s largest market for wind turbines and had begun pitting state owned power companies against each other to see which could build solar plants fastest. According to Gallagher, China initially made forays into clean energy technology as a means for export, but was able to adroitly set up a domestic market when conditions for outward investment and trade turned sour. Today, China is a veritable green power. It leads the world in renewable energy production figures and is the world’s largest producer of wind and solar energy, as well as the largest domestic and outbound investor in renewable energy.

A fear of China drove the U.S. to act on clean energy

Nothing exemplifies the return of Great Power politics more than the race for clean energy. Where decades of activism and warnings from scientists had failed to push significant policy change, fear of falling behind China has created a bipartisan effort to invest in clean energy transition. Together, the CHIPS and Science Act, Infrastructure Deal, and Inflation Reduction Act have brought close to half a trillion dollars in fiscal incentives and investments into the growth of clean energy and low-carbon technologies in the US. However, while the bills will boost research and development, they bring in limited regulation. Gallagher pointed out that according to the Department of Energy’s own estimates, the U.S. is “not competitive” in most clean energy sectors.  It is still too early to tell if the massive influx of capital will be enough for the U.S. to win the clean energy race. “It’s a little daunting for the U.S.,” Gallagher said. Speaking of China’s dominance in the supply chains for low-carbon technologies, she added, “China is a planning economy, and that has really paid off.”

While China maintains an advantage in the race, the U.S. still has some areas where it might have a slight edge.

China seems to have an all-round lead on the U.S. in terms of trade balance, R&D investment, and patenting. On the investment front, China had surpassed the U.S. by the late 2000s and Europe by 2012 to be the world leader in low carbon technology investment, pumping in annually at least US$100 billion into innovation by 2014. As of 2018, Chinese investment in clean energy technology was almost double that of the U.S. (The scenario changes when accounting for nuclear investments by the U.S.) At the same time, China’s State-Owned Enterprises are still making massive investments in fossil fuels that entrench the high-carbon economy and signal a bumpy energy transition for China in the coming future. Still, China’s clean energy leadership is signaled not just by its robust investment, trade, and patent figures but rather the number of clean energy jobs. Arguing that a competitive economy is shown through job creation, Gallagher noted that China has created many jobs in the clean energy sector.

A case study in solar energy:

Nowhere is China’s leading position clearer than in the solar energy sector. China has an estimated 2.7 million people employed in the solar energy sector, making up more than half of the world’s 4.3 million solar jobs. How did China do it? Initially China’s solar industry began in the 1990s to supply Germany’s high demand. The Chinese government provided tax incentives and credits to lure in investors and scoured the globe for supplies of machinery and polysilicon. This vertically integrated supply chain in China created a mammoth solar manufacturing industry that produced more solar panels for less. When western countries imposed tariffs (charging dumping), the Chinese government was able to quickly develop a domestic market. In contrast, the U.S. outsourced its solar production to China. In 2014, the U.S. had a dozen factories producing Photovoltaic wafers and ingots. Today, none exist. According to Gallagher, the disassembling of clean energy infrastructure is making it difficult for the U.S. to claw its way back.

The outlook for the U.S. is grim

Gallagher concluded that U.S. competitiveness in low carbon technology had completely eroded and that Chinese dominance in the clean energy supply chain will not soon go away. China’s carefully operated state machinery, political commitment, and industrial backing will let it keep its advantage in the clean energy race for the years to come.  Gallagher characterized China’s recent investments in fossil fuels as a response to a temporary response to global energy instability. But the direction and momentum of China’s industrial policy will continue China on the path of clean energy dominance. There is one bright spot of hope for the U.S. however: U.S. climate-related spending is due to take off in the next 5 years and will focus on the next step of the clean energy transition: electricity and energy systems integration.

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• Analysis: Clean energy was top driver of China’s economic growth in 2023

Lauri Myllyvirta

Clean energy contributed a record 11.4tn yuan ($1.6tn) to China’s economy in 2023, accounting for all of the growth in investment and a larger share of economic growth than any other sector.

The new sector-by-sector analysis for Carbon Brief, based on official figures, industry data and analyst reports, illustrates the huge surge in investment in Chinese clean energy last year – in particular, the so-called “ new three ” industries of solar power, electric vehicles (EVs) and batteries.

Solar power, along with manufacturing capacity for solar panels, EVs and batteries, were the main focus of China’s clean-energy investments in 2023, the analysis shows.

(For this analysis, we used a broad definition of “clean energy” sectors, including renewables, nuclear power, electricity grids, energy storage, EVs and railways. These are technologies and infrastructure needed to decarbonise China’s production and use of energy.)

Other key findings of the analysis include:

• Clean-energy investment rose 40% year-on-year to 6.3tn yuan ($890bn), with the growth accounting for all of the investment growth across the Chinese economy in 2023.
• China’s $890bn investment in clean-energy sectors is almost as large as total global investments in fossil fuel supply in 2023 – and similar to the GDP of Switzerland or Turkey .
• Including the value of production, clean-energy sectors contributed 11.4tn yuan ($1.6tn) to the Chinese economy in 2023, up 30% year-on-year.
• Clean-energy sectors, as a result, were the largest driver of China’ economic growth overall, accounting for 40% of the expansion of GDP in 2023.
• Without the growth from clean-energy sectors, China’s GDP would have missed the government’s growth target of “ around 5% ”, rising by only 3.0% instead of 5.2% .

The surge in clean-energy investment comes as China’s real-estate sector shrank for the second year in a row. This shift positions the clean-energy industry as a key part not only of China’s energy and climate efforts, but also of its broader economic and industrial policy.

However, the spectre of overcapacity means China’s clean-energy investment growth – and its investment-driven economic model, in general – cannot continue indefinitely.

The growing importance of these new industries gives China a significant economic stake in the global transition to clean-energy technologies.

Yet it also poses questions for overseas policymakers attempting to tie their own climate strategies to domestic industrial growth.

Clean energy drives China’s growth in 2023

Solar power, electric vehicles, energy efficiency, electricity storage and hydrogen, nuclear power, electricity grids, why clean energy took off in 2023, what clean-energy growth means for china – and the world.

China’s clean-energy investment boom means the sector accounted for all of the growth in investment across the country’s economy in 2023, with spending in other areas shrinking.

China invested an estimated 6.3tn yuan ($890bn) in clean-energy sectors in 2023, up from 4.6tn yuan in 2022, a 1.7tn yuan (40%) year-on-year increase. In total, clean energy made up 13% of the huge volume of investment in fixed assets in China in 2023, up from 9% a year earlier.

With Chinese investment growing by just 1.5tn yuan in 2023 overall, the analysis shows that clean energy accounted for all of the growth, while investment in sectors such as real estate shrank.

This is shown in the figure below, which also highlights the concentration of clean-energy investment in the so-called “new three” of solar, energy storage and EVs.

Clean energy was also the top contributor to China’s economic growth overall, contributing around 40% of the year-on-year increase in GDP across all sectors.

Including the value of goods and services, the clean-energy sector contributed an estimated 11.4tn yuan ($1.6tn) to China’s economy in 2023, an increase of 30% year-on-year.

This means clean energy accounted for 9.0% of China’s GDP in 2023, up from 7.2% in 2022.

Without the contribution of clean-energy sectors to China’s economic growth in 2023, the country would have seen its GDP rise by just 3.0%, instead of the 5.2% actually recorded.

This would have missed government growth targets at a time of increasing concerns over the nation’s economic prospects, amid the ongoing real-estate crisis and declining population .

The major role that clean energy played in boosting growth in 2023 means the industry is now a key part of China’s wider economic and industrial development.

This is likely to bolster China’s climate and energy policies – as well as its “ dual carbon ” targets for 2030 and 2060 – by enhancing the economic and political relevance of the sector.

Back to top

The ‘new three’ dominate clean-energy investment

This analysis is based on a combination of government releases, industry data and analyst reports, with the exact methodology varying sector-by-sector, as set out in the sections that follow.

The table below lists the estimated contributions of each sector to Chinese investment and GDP overall in 2023, as well as the year-on-year growth since 2022.

The analysis includes solar, EVs, energy efficiency, rail, energy storage, electricity grids, wind, nuclear and hydropower within the broad category of “clean-energy sectors”. All of these are technologies and infrastructure needed to decarbonise China’s energy supply and consumption.

The so-called “new three” of solar, storage and EVs are all prominent in the table – and all recorded strong growth.

Our analysis shows that investment in clean power generation and energy storage capacity reached 1.7tn yuan in 2023 (up 48% year-on-year), while investment in manufacturing capacity for solar, EVs and batteries reached 2.5tn yuan (+60%).

Investment in clean-energy infrastructure reached 1.4tn yuan (+9%, comprising grids, EV charging points and railways) and investment in energy efficiency was 600bn yuan (+15%).

Meanwhile, our analysis shows the value of production of goods and services in the clean-technology sectors reached 5.1tn yuan in 2023, increasing 26% year-on-year.

This includes the value of electricity generation, EV sales and solar exports, as well as the transport of passengers and goods via rail.

Solar was the largest contributor to growth in China’s clean-technology economy in 2023. It recorded growth worth a combined 1tn yuan of new investment, goods and services, as its value grew from 1.5tn yuan in 2022 to 2.5tn yuan in 2023, an increase of 63% year-on-year.

While China has dominated the manufacturing and installations of solar panels for years, the growth of the industry in 2023 was unprecedented.

On the installation side, two major central government initiatives drove increased volumes, namely the “ whole-county distributed solar ” and the “ clean energy base ” programmes.

In addition, in response to the slowdown in the real-estate sector, the central government introduced a new policy at the start of 2023, to encourage the development of solar power industries on unused and existing construction lands.

Meanwhile, during the annual legislative meetings in the spring of 2023, 15 provinces prioritised solar industry development in their government work agendas.

Detailed data on the growth in China’s solar installations in the first 11 months of the year is shown in the figure below. (An estimated 200GW was added across the country during 2023 as a whole, more than doubling from the record of 87GW set in 2022.)

At the same time, China’s solar manufacturing industry recorded even stronger growth in 2023. China added 340 gigawatts (GW) of polysilicon production capacity and 300GW of wafer, cell and module production capacity in 2023, according to the International Energy Agency (IEA). 

China experienced a significant increase in solar product exports in 2023. It exported 56GW of solar wafers, 32GW of cells and 178GW of modules in the first 10 months of the year, up 90%, 72% and 34% year-on-year respectively, according to the China Photovoltaic Industry Association. However, due to falling costs, the export value of these solar products only increased by 3%.

Within the overall export growth there were notable increases in China’s solar exports to countries along the “belt and road”, to southeast Asian nations and to several African countries .

For this analysis, the value of investments in new solar manufacturing capacity was estimated from the average capital costs of each step in the supply chain, taken from a compilation of reported project costs. This gave a significantly lower cost level than reported in other literature.

The analysis assumes that local government investment in facilities and infrastructure, as well as direct subsidies, added 30% to the reported private investment.

Investment in solar power was estimated by multiplying the newly added capacity from Bloomberg New Energy Finance by the unit investment costs for rooftop and utility-scale systems from China Photovoltaic Industry Association.

The value of exported solar power equipment was based on China Photovoltaic Industry Association data for 2022 and reported export growth for 2023.

The value of solar power equipment produced for domestic installation was not included in our analysis, to avoid overlap with the already-estimated investment costs for domestic solar projects.

China installed 41GW of wind power capacity in the first 11 months of 2023, an increase of 84% year-on-year in new additions. Some 60GW of onshore wind alone was due to be added across 2023, according to China Galaxy Securities, based on trends in previous years.

In addition, offshore wind capacity increased by 6GW across the whole of 2023.

Wind capacity added in the first 11 months of each year is shown in the figure below.

By the end of 2023, the first batch of “clean-energy bases” were expected to have been connected to the grid, contributing to the growth of onshore wind power, particularly in regions such as Inner Mongolia and other northwestern provinces . The second and third batches of clean-energy bases are set to continue driving the growth in onshore wind installations.

The market is also being driven by the “ repowering ” of older windfarms, supported by central government policies promoting the model of replacing smaller, older turbines with larger ones.

The potential for distributed wind power is also being explored, with initiatives such as the “ villages wind utilisation action ” being planned for active implementation.

Progress on offshore wind power construction in 2023 got off to a slow start . This is a reflection of a shift from nearshore to deeper offshore projects and from single projects to larger bases.

Offshore wind projects are also facing complex approval processes, involving multiple regulatory aspects, leading to uncertainties and slower-than-expected installations.

However, these issues are being addressed and the fourth quarter of 2023 saw a rebound in offshore wind construction, with 2024 expected to be a significant year for project deliveries.

Since 2021 , new wind projects in China no longer receive subsidies from the central government.

Despite technological advancements reducing costs, increases in raw material prices have resulted in lower profit margins compared to the solar industry, leading to a smaller overall investment in wind power relative to solar power.

China’s production of electric vehicles grew 36% year-on-year in 2023 to reach 9.6m units , a notable 32% of all vehicles produced in the country.

The vast majority of EVs produced in China are sold domestically, with sales growing strongly despite the phase-out of purchase subsidies announced in 2020 and completed at the end of 2022.

The national purchase subsidy for EVs was a central government finance instrument that had been fostering the EV market for 13 years. Its demise highlights a gradual shift from policy-driven to market-driven demand, making growth more likely to be sustained.

Sales of EVs made in China reached 9.5m units in 2023, a 38% year-on-year increase. Of this total, 8.3m were sold domestically, accounting for one-third of Chinese vehicle sales overall, while 1.2m EVs were exported, a 78% year-on-year increase.

The growth of “new energy vehicle” (NEV, mainly EVs) production and sales is shown in the figure below, which also shows their rising share of all vehicles sold.

China’s EV market is highly competitive, with at least 94 brands offering more than 300 models. Domestic brands account for 81% of the EV market, with BYD, Wuling, Chery, Changan and GAC among the top players.

Sustaining this growth has required major investment in manufacturing capacity.

This analysis estimates investments in EV manufacturing capacity based on a study by China International Association for Promotion of Science and Technology (CIAPST), which put investment in EV manufacturing at 0.7tn yuan in 2021.

The analysis assumes that EVs accounted for all of the growth in investment in vehicle manufacturing capacity reported by China’s national bureau of statistics (NBS) in 2022 and 2023, while investment in conventional vehicles was stable

This implies that investment in EV manufacturing reached CNY 1.2tn yuan in 2023. This is likely to be conservative, because production volumes for combustion engine vehicles are falling, implying a corresponding fall in investment.

This analysis accounts for the expansion of battery manufacturing capacity separately – alongside electricity storage – even though it is being driven by the growth in EV production.

The analysis estimates the value of EV production, including both domestic sales and exports, based on vehicle production volumes from NBS and the reported average EV price.

These EV prices include the value of batteries produced for EVs, so the value of battery production is not included separately.

Meanwhile, EV charging infrastructure is expanding rapidly, enabling the growth of the EV market. In 2022, more than 80% of the downtown areas of “first-tier” cities – megacities such as Beijing, Shanghai and Guangzhou – had installed charging stations, while 65% of the highway service zones nationwide provided charging points.

More than 3m new charging points were put into service during 2023, including 0.93m public and 2.45m private chargers. The accumulated total by November 2023 reached 8.6m charging points.

This analysis puts investment in EV charging infrastructure at 0.1tn yuan in 2023, based on an estimated average cost of 30,000 yuan per charging point.

China’s energy intensity reduction targets have put pressure on industries to reduce their energy use per unit of output, spurring investment in more efficient processes.

For this analysis, the size of the market for energy service companies is used as a proxy for investment in energy efficiency in industries and buildings. This market grew to an estimated 0.6tn yuan in 2023, up from 0.5tn yuan in 2022, based on the revenue growth of the top 10 listed energy service companies ranked by market capitalization, for the first two or three quarters of 2023.

Over the past two decades, China’s energy service sector has experienced rapid expansion, growing from 1.8bn yuan in 2003 to 607bn yuan in 2021. Investment in the industrial service sector has been a key driver, accounting for about 60% of the total investment.

However, 2022 saw a significant downturn in the industrial energy service output, influenced by poor industrial growth , even though the building service sector continued expanding.

This analysis puts China’s investment in building energy efficiency at 80bn yuan per year. The country’s 14th five-year plan for energy savings in buildings and development of “green buildings” targets 80m square metres per year of renovated and newly built green buildings.

Compared with the almost 1,000m square metres of building space completed annually, this is a small percentage, and accordingly, the estimated value of total investments is modest.

China is rapidly scaling up electricity storage capacity. This has the potential to significantly reduce China’s reliance on coal- and gas-fired power plants to meet peaks in electricity demand and to facilitate the integration of larger amounts of variable wind and solar power into the grid.

The construction of pumped hydro storage capacity increased dramatically in the last year, with capacity under construction reaching 167GW, up from 120GW a year earlier.

This growth is illustrated in the figure below, which shows pumped hydro capacity under construction or in earlier stages of development at the end of 2023.

Data from Global Energy Monitor identifies another 250GW in pre-construction stages, indicating that there is potential for the current surge in capacity to continue.

For this analysis, estimated annual investments in pumped storage are assumed to be proportional to the capacity under construction, while the reported construction cost of 6 yuan per watt is spread over three years. This implies that investment in 2023 amounted to 0.3tn yuan.

Construction of new battery manufacturing capacity was another major driver of investments, estimated at 0.3tn. This is based on the added capacity reported by the China Automotive Power Battery Industry Innovation Alliance and estimated average investment costs per unit of production capacity, taken from a compilation of publicly reported project costs.

Investment in electrolysers for “green” hydrogen production almost doubled year-on-year in 2023, reaching approximately 90bn yuan, based on estimates for the first half of the year from SWS Research. Analyst reports and compilations of projects published in news media put far larger numbers on China’s investments in green hydrogen, but these generally include the spending on electricity generation, which in this analysis is accounted for separately.

Investment in “new energy storage technologies” – a classification dominated by batteries – more than doubled in 2023, reaching 75bn yuan. This estimate is based on newly added capacity in 2023 reported by China Energy Storage Alliance and average investment costs calculated from National Energy Administration data .

China’s ministry of transportation reported that investment in railway construction increased 7% in January–November 2023, implying investment of 0.8tn for the full year. This includes major investments in both passenger and freight transport. Investment in roads fell slightly, while investment in railways overall grew by 22%.

The share of freight volumes transported by rail in China has increased from 7.8% in 2017 to 9.2% in 2021, thanks to the rapid development of the railway network.

In 2022, some 155,000km of rail lines were in operation , of which 42,000km were high-speed. This is up from 146,000km of which 38,000km were high-speed in 2020.

The value of passenger and freight transportation on China’s railways increased by 39% year-on-year in 2023, reaching nearly 1tn yuan.

In 2023, 10 nuclear power units were approved in China, exceeding the anticipated rate of 6-8 units per year set by the China Nuclear Energy Association in 2020 for the second year in a row.

There are 77 nuclear power units that are currently operating or under construction in China, the second-largest total in the world. The total yearly investment in 2023 was estimated for this analysis at 87bn yuan, an increase of 45% year-on-year, based on data for January–November from the National Energy Administration.

The highest numbers of nuclear projects are located in coastal provinces with large concentrations of heavy industry, such as Guangdong, Fujian and Zhejiang, as the development of inland nuclear power projects remains stalled .

These provinces get around 20% of their electricity from nuclear power and continue to expand the technology as part of their efforts to cut emissions from their power sectors. 

China’s power-sector development plans include a major increase in inter-provincial electricity transmission capacity and numerous long-distance transmission lines from west to east.

State Grid, the government-owned operator that runs the majority of the country’s electricity transmission network, has a target to raise inter-provincial power transmission capacity to 300GW by 2025 and 370GW by 2030, from 230GW in 2021. These plans play a major role in enabling the development of clean energy bases in western China.

China Electricity Council reported investments in electricity transmission at 0.5tn yuan in 2023, up 8% on year – just ahead of the level targeted by State Grid.

The clean-energy investment boom in 2023 is the outcome of a major pivot in China’s macroeconomic strategy . As this analysis shows, investment flowed from real estate into manufacturing – primarily in the clean-energy sector.

Total investment in the manufacturing industry increased by 9% year-on-year in 2023, while investment in the power and heat sectors climbed 23%. These increases were entirely due to growth in investment in clean energy, with investment in other areas falling. Therefore, China’s pivot into manufacturing was, in reality, a pivot to cleantech manufacturing.

The reason for this pivot was the contraction in the real-estate sector, where investment fell by 10% year-on-year in 2022 and another 9% in 2023. While this drop was in line with the government’s aim to address financial risks and excess leverage in the sector, it left a major hole in aggregate investment demand and in the revenue of China’s local governments.

Local governments were under pressure to attract investment, meaning that they offered generous subsidies and helped arrange financing.

The central government, for its part, eased private-sector access to financial markets and bank loans during the Covid-19 pandemic, facilitating the growth of the clean-energy sector.

Unlike the state-owned firms dominating traditional industries, the low-carbon sector, largely composed of private companies, gained access to previously constrained credit.

The significance of this economic shift is reflected not only in the figures revealed by this analysis but also in the language being used by Chinese media.

The three largest of clean-energy sectors by value, namely solar, storage and EVs, are being referred to as the “new three”, in contrast to the “old three” – clothing, home appliances and furniture.

This pivot was only possible because China’s clean-energy policies and wider industrial policy had built the foundation and scaled up these sectors so that they were primed for rapid growth.

The post-Covid credit “push” for clean energy growth also coincided with a demand “pull”, driven by falling costs and the increased competitiveness of low-carbon technologies against fossil fuels due to technological advancements.

Moreover, the announcement in 2020 of the 2060 carbon neutrality target had raised expectations and provided the political signal for the scale-up.

Clean technology has been an important part of China’s energy policy, industrial strategy and climate change efforts for a long time. Last year marked the first time that the sector also became a key economic driver for the country. This has important implications.

China’s reliance on the clean-technology sectors to drive growth and achieve key economic targets boosts their economic and political importance. It could also support an accelerated energy transition.

The massive investment in clean technology manufacturing capacity and exports last year means that China has a major stake in the success of clean energy in the rest of the world and in building up export markets.

For example, China’s lead climate negotiator Su Wei recently highlighted that the goal of tripling renewable energy capacity globally, agreed in the COP28 UN climate summit in December, is a major benefit to China’s new energy industry. This will likely also mean that China’s efforts to finance and develop clean energy projects overseas will intensify.

Globally, China’s unprecedented clean-energy manufacturing boom has pushed down prices, with the cost of solar panels falling 42% year-on-year – a dramatic drop even compared to the historical average of around 17% per year, while battery prices fell by an even steeper 50%.

This, in turn, has encouraged much faster take-up of clean-energy technologies.

Projections of solar power deployment, in particular, have been upended. The IEA’s latest World Energy Outlook introduced an additional global energy scenario just to look at the implications, projecting that if global deployment of solar power and grid-connected batteries follows the expansion of manufacturing capacity, then global power-sector coal use and carbon dioxide emissions could be a sizable 15% lower than in the base case by 2030. Most of the additional deployment of solar in the IEA’s revised projections is in China.

Even with the increased deployment, however, there is a limit to how much solar power, batteries and other clean technology can be absorbed, as the manufacturing expansion has already saturated most of the global market.

This means that the expansion will run into overcapacity, if maintained. On the other hand, in order to keep driving growth in investment, clean-technology manufacturing would need to not only absorb as much capital as it did in 2023, but keep increasing investment year after year.

The clean-technology investment boom has provided a new lease of life to China’s investment-led economic model. There are new clean-energy technologies where there is scope for expansion, such as electrolysers .

Eventually, however, entirely new sectors will have to be found for investment – or China’s economic model will have to be transformed once there is nowhere left for investment to flow.

The manufacturing boom also cements China’s dominant position in clean-energy supply chains. Other countries therefore face a choice of whether they want to benefit from the low-cost supply of solar panels, batteries, EVs and other clean-energy technology from China.

The alternative is diversifying their supply and paying the cost of building new supply chains, in the form of subsidies and import tariffs required to enable domestic producers or producers in third countries to compete against Chinese suppliers. Such efforts would further increase supply and push down global prices even further.

Analysis: China’s CO2 falls 1% in Q2 2024 in first quarterly drop since Covid-19

‘Critical turning point’ for coal poses risks for China’s state power firms, says report

分析：中国清洁能源发展使五月燃煤发电份额降至53%的历史低点

Analysis: China’s clean energy pushes coal to record-low 53% share of power in May 2024

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Case-study of a coal gasification-based energy supply system for China

• Chemical & Biological Engineering
• Andlinger Center for Energy & the Environment
• Princeton School of Public and International Affairs

Research output : Contribution to journal › Article › peer-review

"Syngas city" (SC) is a concept for a coal gasification-based energy supply system that deploys gasification-based polygeneration technologies to meet energy needs of coal-rich areas. This paper summarizes an assessment of the projected environmental impacts of implementing a SC strategy for Zaozhuang, Shandong Province, China. A SC scenario and a "business-as-usual" (BAU) scenario are developed for the Zaozhuang area considering the time-frame 2000 to 2020. A comparison of these scenarios is used to assess whether the SC concept for Zaozhuang could reduce air pollution and promote further economic development while meeting projected demand for energy services. On the basis of socio-economic assumptions, sectoral energy-demand projections are developed. Assumptions are made about expected rates of market penetration of dimethyl ether (DME) and methanol, two clean fuels derived via coal gasification. Emissions of air pollutants in the SC scenario are compared with those in the BAU scenario. Policies to promote the SC concept and technologies in China are proposed.

All Science Journal Classification (ASJC) codes

• Geography, Planning and Development
• Renewable Energy, Sustainability and the Environment
• Management, Monitoring, Policy and Law

Access to Document

• 10.1016/S0973-0826(08)60380-4

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• Link to publication in Scopus
• Link to the citations in Scopus

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• Energy Supply System Keyphrases 100%
• City Scenarios Keyphrases 33%
• Air Pollutant Emissions Keyphrases 16%
• Energy Demand Projection Keyphrases 16%
• City Strategy Keyphrases 16%
• Shandong Province Keyphrases 16%
• Rich Coal Engineering 16%
• Sectoral Engineering 16%

T1 - Case-study of a coal gasification-based energy supply system for China

AU - Hongtao, Zheng

AU - Zheng, Li

AU - Weidou, Ni

AU - Larson, Eric D.

AU - Tingjin, Ren

N1 - Funding Information: Financial support is gratefully acknowledged from BP (via the ‘‘Clean Energy Facing the Future’’ program at Tsinghua University), BP/Ford (via the ‘‘Carbon Mitigation Initiative’’ at Princeton University), the William and Flora Hewlett Foundation, the Blue Moon Fund, and the Task Force on Energy Strategies and Technologies of the China Council for International Cooperation on Environment and Development.

PY - 2003/12

Y1 - 2003/12

N2 - "Syngas city" (SC) is a concept for a coal gasification-based energy supply system that deploys gasification-based polygeneration technologies to meet energy needs of coal-rich areas. This paper summarizes an assessment of the projected environmental impacts of implementing a SC strategy for Zaozhuang, Shandong Province, China. A SC scenario and a "business-as-usual" (BAU) scenario are developed for the Zaozhuang area considering the time-frame 2000 to 2020. A comparison of these scenarios is used to assess whether the SC concept for Zaozhuang could reduce air pollution and promote further economic development while meeting projected demand for energy services. On the basis of socio-economic assumptions, sectoral energy-demand projections are developed. Assumptions are made about expected rates of market penetration of dimethyl ether (DME) and methanol, two clean fuels derived via coal gasification. Emissions of air pollutants in the SC scenario are compared with those in the BAU scenario. Policies to promote the SC concept and technologies in China are proposed.

AB - "Syngas city" (SC) is a concept for a coal gasification-based energy supply system that deploys gasification-based polygeneration technologies to meet energy needs of coal-rich areas. This paper summarizes an assessment of the projected environmental impacts of implementing a SC strategy for Zaozhuang, Shandong Province, China. A SC scenario and a "business-as-usual" (BAU) scenario are developed for the Zaozhuang area considering the time-frame 2000 to 2020. A comparison of these scenarios is used to assess whether the SC concept for Zaozhuang could reduce air pollution and promote further economic development while meeting projected demand for energy services. On the basis of socio-economic assumptions, sectoral energy-demand projections are developed. Assumptions are made about expected rates of market penetration of dimethyl ether (DME) and methanol, two clean fuels derived via coal gasification. Emissions of air pollutants in the SC scenario are compared with those in the BAU scenario. Policies to promote the SC concept and technologies in China are proposed.

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UR - http://www.scopus.com/inward/citedby.url?scp=21744436014&partnerID=8YFLogxK

U2 - 10.1016/S0973-0826(08)60380-4

DO - 10.1016/S0973-0826(08)60380-4

M3 - Article

AN - SCOPUS:21744436014

SN - 0973-0826

JO - Energy for Sustainable Development

JF - Energy for Sustainable Development

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Please note you do not have access to teaching notes, low carbon supply chain with energy consumption constraints: case studies from china’s textile industry and simple analytical model.

Supply Chain Management

ISSN : 1359-8546

Article publication date: 8 May 2017

This paper aims to discuss the low carbon supply chain practices in China’s textile industry. To curb greenhouse gas emissions, the Chinese government has launched restrict regulatory system and imposed the energy consumption constraint in the textile industry to guarantee the achievability of low carbon economy. The authors aim to examine how the energy consumption constraint affects the optimal decisions of the supply chain members and address the supply chain coordination issue.

Design/methodology/approach

The authors conduct two case studies from Chinese textile companies and examine the impact of energy consumption constraints on their production and operations management. Based on the real industrial practices, the authors then develop a simple analytical model for a low carbon supply chain in which it consists of one single retailer and one single manufacturer, and the manufacturer determines the choice of clean technology for energy efficiency improvement and emission reduction.

From the case studies, the authors find that the textile companies develop clean technologies to reduce carbon emission in production process under the energy consumption enforcement. In this analytical model, the authors derive the optimal decisions of the supply chain members and reveal that supply chain coordination can be achieved if the manufacturer properly sets the reservation wholesale price (WS) despite the production capacity can fulfill partial market demand under a WS (or cost sharing) contract. The authors also find that the cost-sharing contract may induce the manufacturer to increase the investment of clean technology and reduce the optimal WS.

Originality/value

This paper discusses low carbon supply chain practices in China’s textile industry and contributes toward green supply chain development. Managerial implications are identified, which are beneficial to the entire textile industry in the developing countries.

• Energy consumption
• Textile supply chain

Shen, B. , Ding, X. , Chen, L. and Chan, H.L. (2017), "Low carbon supply chain with energy consumption constraints: case studies from China’s textile industry and simple analytical model", Supply Chain Management , Vol. 22 No. 3, pp. 258-269. https://doi.org/10.1108/SCM-05-2015-0197

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Spatial–temporal evolution and improvement measures of embodied carbon emissions in interprovincial trade for coal energy supply bases: case study of anhui, china.

1. Introduction

2. study area, 3. materials and methods, 3.1. mrio model, 3.2. eces-ipt estimation model, 3.3. coupling relationship (cr) model, 3.4. data sources, 4.1. changes in the net eces-ipt outflow in anhui among the yreb, 4.2. spatial–temporal evolution of the net eces-ipt outflow in anhui among the yreb, 4.3. industry sectors characteristics of net eces-ipt outflow in anhui among the yreb, 5. discussion, 5.1. improvement measures for eces-ipt reduction of different industry sectors, 5.1.1. classification for industry sector by cr model, 5.1.2. improvement measures for different industrial types, 5.2. further improvement measures of different target provinces in major industry sectors, 5.2.1. improvement measures for elecmachinery, 5.2.2. improvement measures for coalmin, 5.2.3. improvement measures for nonmprod, 6. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• IPCC. Climate Change 2022: Impacts, Adaptation and Vulnerability ; Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Ed.; Cambridge University Press: Cambridge, UK, 2022. [ Google Scholar ]
• Jägermeyr, J.; Müller, C.; Ruane, A.C.; Elliott, J.; Balkovic, J.; Castillo, O.; Rosenzweig, C. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat. Food 2021 , 2 , 873–885. [ Google Scholar ] [ CrossRef ]
• Chen, S.; Chen, B.; Feng, K.; Liu, Z.; Fromer, N.; Tan, X.; Hubacek, K. Physical and virtual carbon metabolism of global cities. Nat. Commun. 2020 , 11 , 1–11. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Hong, C.; Zhang, Q.; Zhang, Y.; Davis, S.J.; Tong, D.; Zheng, Y.; Schellnhuber, H.J. Impacts of climate change on future air quality and human health in China. Proc. Natl. Acad. Sci. USA 2019 , 116 , 17193–17200. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Dong, S.; Wang, Z.; Li, Y.; Li, F.; Li, Z.; Chen, F.; Cheng, H. Assessment of comprehensive effects and optimization of a circular economy system of coal power and cement in Kongtong District, Pingliang City, Gansu Province, China. Sustainability 2017 , 9 , 787. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wang, C.; Zhao, Y.; Wang, Y.; Wood, J.; Kim, C.Y.; Li, Y. Transportation CO 2 emission decoupling: An assessment of the Eurasian logistics corridor. Transp. Res. Part D Transp. Environ. 2020 , 86 , 102486. [ Google Scholar ] [ CrossRef ]
• Tian, K.; Zhang, Y.; Li, Y.; Ming, X.; Jiang, S.; Duan, H.; Wang, S. Regional trade agreement burdens global carbon emissions mitigation. Nat. Commun. 2022 , 13 , 1–12. [ Google Scholar ] [ CrossRef ]
• Cheng, H.; Dong, S.; Li, F.; Yang, Y.; Li, S.; Li, Y. Multiregional input-output analysis of spatial-temporal evolution driving force for carbon emissions embodied in interprovincial trade and optimization policies: Case study of northeast industrial district in China. Environ. Sci. Technol. 2018 , 52 , 346–358. [ Google Scholar ] [ CrossRef ]
• Chen, Z.; Ni, W.; Xia, L.; Zhong, Z. Structural decomposition analysis of embodied carbon in trade in the middle reaches of the Yangtze River. Environ. Sci. Pollut. Res. 2019 , 26 , 816–832. [ Google Scholar ] [ CrossRef ]
• Clarke-Sather, A.; Qu, J.; Wang, Q.; Zeng, J.; Li, Y. Carbon inequality at the sub-national scale: A case study of provincial-level inequality in CO 2 emissions in China 1997–2007. Energy Policy 2011 , 39 , 5420–5428. [ Google Scholar ] [ CrossRef ]
• Liu, H.; Liu, W.; Fan, X.; Liu, Z. Carbon emissions embodied in value added chains in China. J. Clean. Prod. 2015 , 103 , 362–370. [ Google Scholar ] [ CrossRef ]
• Li, F. Progress and prospects of research on transfer of carbon emissions embodied in inter-regional trade. Prog. Geogr. 2018 , 37 , 1303–1313. (In Chinese) [ Google Scholar ]
• Balistreri, E.J.; Kaffine, D.T.; Yonezawa, H. Optimal environmental border adjustments under the General Agreement on Tariffs and Trade. Environ. Resour. Econ. 2019 , 74 , 1037–1075. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ji, X.; Liu, Y.; Han, M.; Meng, J. The mutual benefits from Sino-Africa trade: Evidence on emission transfer along the global supply chain. J. Environ. Manag. 2020 , 263 , 110332. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hou, J.; Deng, X.; Springer, C.H.; Teng, F. A global analysis of CO 2 and non-CO 2 GHG emissions embodied in trade with Belt and Road Initiative countries. Ecosyst. Health Sustain. 2020 , 6 , 1761888. [ Google Scholar ] [ CrossRef ]
• Wood, R.; Neuhoff, K.; Moran, D.; Simas, M.; Grubb, M.; Stadler, K. The structure, drivers and policy implications of the European carbon footprint. Clim. Policy 2020 , 20 (Suppl. S1), S39–S57. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Cai, X.; Che, X.; Zhu, B.; Zhao, J.; Xie, R. Will developing countries become pollution havens for developed countries? An empirical investigation in the Belt and Road. J. Clean. Prod. 2018 , 198 , 624–632. [ Google Scholar ] [ CrossRef ]
• Li, Y.; Sun, L.; Zhang, H.; Liu, T.; Fang, K. Does industrial transfer within urban agglomerations promote dual control of total energy consumption and energy intensity? J. Clean. Prod. 2018 , 204 , 607–617. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Zhao, T.; Wang, J.; Wei, Y. The embodied CO 2 transfer across sectors of cities in Jing-Jin-Ji region: Combining multi-regional input–output analysis with complex network analysis. Environ. Sci. Pollut. Res. 2021 , 28 , 44249–44263. [ Google Scholar ] [ CrossRef ]
• Yang, Y.; Dong, S.; Li, F.; Cheng, H.; Li, Z.; Li, Y.; Li, S. An analysis on the adoption of an interregional carbon emission reduction allocation approach in the context of China’s interprovincial carbon emission transfer. Environ. Dev. Sustain. 2021 , 23 , 4385–4411. [ Google Scholar ] [ CrossRef ]
• Yuan, X.; Sheng, X.; Chen, L.; Tang, Y.; Li, Y.; Jia, Y.; Zuo, J. Carbon footprint and embodied carbon transfer at the provincial level of the Yellow River Basin. Sci. Total Environ. 2022 , 803 , 149993. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Li, J.; Yang, Y. Strategic adjustment of land use policy under the economic transformation. Land Use Policy 2018 , 74 , 5–14. [ Google Scholar ] [ CrossRef ]
• Xiao, R.; Tan, G.; Huang, B.; Li, J.; Luo, Y. Pathways to sustainable development: Regional integration and carbon emissions in China. Energy Rep. 2022 , 8 , 5137–5145. [ Google Scholar ] [ CrossRef ]
• Lu, Y.; Zhang, Y.; Cao, X.; Wang, C.; Wang, Y.; Zhang, M.; Zhang, Z. Forty years of reform and opening up: China’s progress toward a sustainable path. Sci. Adv. 2019 , 5 , eaau9413. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Yang, Y.; Liu, Y.; Li, Y.; Li, J. Measure of urban-rural transformation in Beijing-Tianjin-Hebei region in the new millennium: Population-land-industry perspective. Land Use Policy 2018 , 79 , 595–608. [ Google Scholar ] [ CrossRef ]
• Ma, W.; Jiang, G.; Chen, Y.; Qu, Y.; Zhou, T.; Li, W. How feasible is regional integration for reconciling land use conflicts across the urban–rural interface? Evidence from Beijing–Tianjin–Hebei metropolitan region in China. Land Use Policy 2020 , 92 , 104433. [ Google Scholar ] [ CrossRef ]
• Chen, Y.; Zhang, S.; Huang, D.; Li, B.L.; Liu, J.; Liu, W.; Wang, H. The development of China’s Yangtze River Economic Belt: How to make it in a green way. Sci. Bull. 2017 , 62 , 648–651. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Tian, Y.; Sun, C. Comprehensive carrying capacity, economic growth and the sustainable development of urban areas: A case study of the Yangtze River Economic Belt. J. Clean. Prod. 2018 , 195 , 486–496. [ Google Scholar ] [ CrossRef ]
• Hui, E.C.; Li, X.; Chen, T.; Lang, W. Deciphering the spatial structure of China’s megacity region: A new bay area—The Guangdong-Hong Kong-Macao Greater Bay Area in the making. Cities 2020 , 105 , 102168. [ Google Scholar ] [ CrossRef ]
• Gao, X.; Zhang, A.; Sun, Z. How regional economic integration influence on urban land use efficiency? A case study of Wuhan metropolitan area, China. Land Use Policy 2020 , 90 , 104329. [ Google Scholar ] [ CrossRef ]
• Tang, L.; He, G. How to improve total factor energy efficiency? An empirical analysis of the Yangtze River economic belt of China. Energy 2021 , 235 , 121375. [ Google Scholar ] [ CrossRef ]
• Liu, J.; Tian, Y.; Huang, K.; Yi, T. Spatial-temporal differentiation of the coupling coordinated development of regional energy-economy-ecology system: A case study of the Yangtze River Economic Belt. Ecol. Indic. 2021 , 124 , 107394. [ Google Scholar ] [ CrossRef ]
• Wang, P.; Wu, J. Impact of environmental investment and resource endowment on regional energy efficiency: Evidence from the Yangtze River Economic Belt, China. Environ. Sci. Pollut. Res. 2022 , 29 , 5445–5453. [ Google Scholar ] [ CrossRef ]
• Yu, S.; Wei, Y.M.; Fan, J.; Zhang, X.; Wang, K. Exploring the regional characteristics of inter-provincial CO2 emissions in China: An improved fuzzy clustering analysis based on particle swarm optimization. Appl. Energy 2012 , 92 , 552–562. [ Google Scholar ] [ CrossRef ]
• Yang, N.; Tang, S.; Zhang, S.; Huang, W.; Chen, P.; Chen, Y.; Wang, K. Fluorine in Chinese coal: A review of distribution, abundance, modes of occurrence, genetic factors and environmental effects. Minerals 2017 , 7 , 219. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhu, A.; Wang, Q.; Liu, D.; Zhao, Y. Analysis of the characteristics of CH4 emissions in China’s coal mining industry and research on emission reduction measures. Int. J. Environ. Res. Public Health 2022 , 19 , 7408. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, R.; Liu, W.; Xiao, L.; Liu, J.; Kao, W. Path towards achieving of China’s 2020 carbon emission reduction target—A discussion of low-carbon energy policies at province level. Energy Policy 2011 , 39 , 2740–2747. [ Google Scholar ] [ CrossRef ]
• International Energy Agency. World Energy Balances 2016 ; IEA Publications: Paris, France, 2016. [ Google Scholar ]
• International Energy Agency. China’s Engagement in Global Energy Governance ; IEA Publications: Paris, France, 2016. [ Google Scholar ]
• Ang, B.W.; Wang, H. Index decomposition analysis with multidimensional and multilevel energy data. Energy Econ. 2015 , 51 , 67–76. [ Google Scholar ] [ CrossRef ]
• Chen, Z.M.; Chen, G.Q.; Chen, B. Embodied carbon dioxide emissions of the world economy: A systems input-output simulation for 2004. Procedia Environ. Sci. 2010 , 2 , 1827–1840. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhao, N.; Xu, L.X.; Malik, A.; Song, X.G.; Wang, Y.F. Inter-provincial trade driving energy consumption in China. Resour. Conserv. Recycl. 2018 , 134 , 329–335. [ Google Scholar ] [ CrossRef ]
• Ministry of Natural Resources, PRC. 2022 China Mineral Resources ; Geological Publishing House: Beijing, China, 2022; p. 18.
• The CPC Central Committee. The State Council, P.R.C. Outline of the Yangtze River Delta Regional Integrated Development Plan ; The CPC Central Committee: Beijing, China, 2019. (In Chinese)
• The CPC Anhui Provincial Committee. Suggestions of the CPC Anhui Provincial Committee on Formulating the 14th Five-Year Plan and Long-Term Goals for National Economic and Social Development ; The CPC Anhui Provincial Committee: Hefei, China, 2020. (In Chinese)
• Kuznets, S. The Structure of the American Economy, 1919–1929. By Wassily W. Leontief. Harvard University Press: Cambridge, UK, 1941. pp. xi, 181. $ 2.50. J. Econ. Hist. 1941 , 1 , 246. [ Google Scholar ] [ CrossRef ]
• Leontief, W. Environmental repercussions and the economic structure: An input-output approach. Rev. Econ. Stat. 1970 , 52 , 262–271. [ Google Scholar ] [ CrossRef ]
• Lin, B.; Sun, C. Evaluating carbon dioxide emissions in international trade of China. Energy Policy 2010 , 38 , 613–621. [ Google Scholar ] [ CrossRef ]
• Li, S.; Qi, S.; He, J. China Regional Expansion Input-Output Table in 2007: Preparation and Application ; Economic Science Press: Beijing, China, 2016. (In Chinese) [ Google Scholar ]
• Li, S.; Pan, C.; He, J. China Regional Expansion Input-Output Table in 2017: Preparation and application ; Economic Science Press: Beijing, China, 2021. (In Chinese) [ Google Scholar ]
• IPCC. IPCC Guidelines for National Greenhouse Gas Inventories ; IGES: Kanagawa, Japan, 2006. [ Google Scholar ]
• Peters, G.P.; Weber, C.; Liu, J. Construction of Chinese Energy and Emissions Inventory ; NTNU: Trondheim, Norway, 2006. [ Google Scholar ]
• National Bureau of Statistics of the People’s Republic of China. Energy Statistics Knowledge Manual ; China Statistics Press: Beijing, China, 2006.

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Zhang, M.; Dong, S.; Li, F.; Xu, S.; Guo, K.; Liu, Q. Spatial–Temporal Evolution and Improvement Measures of Embodied Carbon Emissions in Interprovincial Trade for Coal Energy Supply Bases: Case Study of Anhui, China. Int. J. Environ. Res. Public Health 2022 , 19 , 17033. https://doi.org/10.3390/ijerph192417033

Zhang M, Dong S, Li F, Xu S, Guo K, Liu Q. Spatial–Temporal Evolution and Improvement Measures of Embodied Carbon Emissions in Interprovincial Trade for Coal Energy Supply Bases: Case Study of Anhui, China. International Journal of Environmental Research and Public Health . 2022; 19(24):17033. https://doi.org/10.3390/ijerph192417033

Zhang, Menghan, Suocheng Dong, Fujia Li, Shuangjie Xu, Kexin Guo, and Qian Liu. 2022. "Spatial–Temporal Evolution and Improvement Measures of Embodied Carbon Emissions in Interprovincial Trade for Coal Energy Supply Bases: Case Study of Anhui, China" International Journal of Environmental Research and Public Health 19, no. 24: 17033. https://doi.org/10.3390/ijerph192417033

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Seizing China’s energy-efficiency opportunity: A case study

Energy efficiency and conservation have rocketed up China’s corporate agenda, particularly for heavy-industry players such as power plants, steelmakers, chemical companies, and automakers. Energy is the largest expense for some of these industries, and since variable costs represent a larger share of total costs in China than in more developed countries, where fixed labor outlays are higher, volatile commodity prices hit China’s core industrials much harder. These economic fundamentals apply to multinationals and local players alike, so efforts to secure the benefits of improved energy efficiency are important for a wide cross-section of companies.

Yet achieving those benefits is difficult. The tendency at most industrial companies, and not just in China, is to equate energy savings with capital expenditures, hardware, and other technical solutions. Actually, what is often most important to change is poor cooperation and unhelpful mind-sets prevalent on the front line. Similarly, many companies in China and elsewhere lack an integrated view of how energy yields, energy output, and energy consumption combine to affect their operations. Some measure these factors only in a superficial way.

Nonetheless, a few of China’s leading industrial players are making impressive headway. In this article, we’ll look at one such company—a large resource- and emission-intensive Chinese state-owned enterprise—that in the wake of the global financial crisis began rolling out a series of energy-efficiency improvements across its plant network. A closer look at the company’s flagship plant, where energy consumption fell by more than 10 percent, offers insights for other industrial groups, in China and beyond, as they seek ways to lower costs and use energy resources more wisely.

Welcome to the downturn

As consumer demand plummeted at the start of the global economic downturn, the company’s leaders watched as prices for its goods fell by more than 50 percent in a matter of weeks. Within four months, the group’s record figure for profits was followed by a comparable loss.

To stanch the bleeding, the company’s leaders launched an aggressive operational-improvement effort. To no one’s surprise, energy efficiency appeared the likeliest starting place—after all, energy was the biggest cost driver, representing half of a plant’s variable costs and about 40 percent of the total. Personnel costs, by contrast, were less than 8 percent of the total. Only by improving energy efficiency, the leaders believed, could the company hope to regain profitability and put its operations on a more solid footing.

‘Energy is free’

The team of company experts these executives assembled to assess the situation faced an immediate hurdle: no one at the plant level was responsible for tracking energy in the necessary detail. Even at the group level, the company had little visibility into the way energy consumption, yields, and output combined to affect the economics or operations of plants. At the company’s flagship facility, only one employee worked on energy-related issues—part time—and he focused on basic monitoring and on collecting data for government-reporting purposes, not on efficiency improvements.

This state-owned company’s inattention to energy efficiency is far from unusual in China, and far more common in industrial environments around the world than you might expect. The reason is that the costs associated with energy use often are felt, if they are felt at all, far from the factory floor, where energy is consumed. Most of the Chinese company’s line workers thought of energy as “free,” when they bothered to think of it—a sentiment we hear across shop floors around the world. At this company, that mindset encouraged well-meaning yet shortsighted activities. On the front line, for example, workers used compressed air to cool down motors and extend their operating lives, although on an annualized basis the compressed air cost several times more than a new motor.

As company experts began to work closely with leaders at the flagship plant to gather data and identify opportunities, they quickly encountered another mind-set challenge common to operational-improvement settings: complacency. The leaders of the plant knew full well that it was the pride of the group, and many believed that its efficiency approached or matched global standards on some measures. Only a few percentage points of improvement were possible, many thought, and new equipment would be needed to realize energy-efficiency gains. This attitude was shared throughout the plant. “We thought we were already the best in China,” said one worker. “We were running at our technical limits,” said another.

Wake-up calls

Two events began turning the tide. First, a benchmarking effort showed that the flagship plant was squarely in the middle of the pack when ranked against global competitors. The company’s best wasn’t good enough.

Second, the company’s CEO decided to pay a surprise visit to the facility. He recognized that seizing energy-efficiency opportunities would require determination and a new way of thinking about operations and wanted to see the starting point first hand. He also hoped to send a clear signal—to plant leaders and workers alike—that he was serious about change.

Leaving his company car and driver at his hotel to avoid tipping off the plant’s staff, the CEO set out with two others in a private car late one evening to observe the night shift. After spending nearly 20 minutes locating a supervisor in the guts of the vast plant, the CEO was dismayed to find no one working in an area of its coal-gasification 1 1. Coal gasification is a common process, used in heavy-industry settings such as chemicals, steelmaking, and oil refining, to extract fuel energy from coal. unit where employees should have been making energy-saving temperature adjustments. Instead, these workers were visiting with colleagues in a control room. One detail illustrated the lack of seriousness some of them showed in approaching the energy challenge: a maintenance checklist bore a signature indicating that an inspection had been completed at 5 AM the following morning. It was not quite midnight.

A similar visit later that week to a nearby satellite facility, while not as dramatic as the first one, also drove home the need for change. A week later, the CEO announced a wholesale replacement of the plant’s leadership, in an effort to impose the management discipline needed for energy-efficiency efforts.

Getting down to business

Following these wake-up calls, managers and workers began buckling down. In the plant’s coal-gasification unit, for example, the company rationalized the way coal was transported and stored. Coal begins to oxidize and degrade as soon as it’s mined, but through better handling and a straightforward “first in, first out” system, the company improved the energy yield of its coal significantly.

Meanwhile, a better screening system ensured that coal particles were more uniform in size, which improved the efficiency of gasification. Finally, better management and tracking in the coal yard helped the company reduce inventory from 20 days to 10. All told, these changes—plus comparable moves to make the boilers, turbines, and other steam-related equipment more efficient—helped reduce costs in this area by 13 percent (and by 7 percent in the first month alone).

The company launched similar efforts to improve the efficiency of motors, pumps, and other equipment vital to plant operations. Like the changes to the steam-related processes, most of these improvements will require little in the way of capital investment. To date, company executives have identified a potential 15 percent improvement in this area and expect to fully achieve (or exceed) it within 12 months.

Measure, then manage

To help ensure that the changes would stick, the company implemented rigorous data-gathering and performance-management systems alongside the operational changes. Earlier, it hadn’t measured energy use in any of the plant’s large operational processes. Today it measures all of them. Improved tracking and straightforward shop-floor kanbans (signboards that help workers visualize work flow and trigger activities that enhance fast responses) help workers monitor temperatures, processes, and tolerances to maximize energy efficiency. The plant also conducts “theoretical limit” analyses to see what best performance looks like—an exercise that lets workers determine where and how to focus and quantify their efforts.

Efficiency targets are now tied to the performance appraisals of plant managers. Similarly, managers and workers who have direct control over underlying factors that drive energy efficiency—say, the operating temperature of a mechanical process—are assigned as “owners,” with direct responsibility for meeting targets. Daily performance dialogues help workers keep on track while giving them a forum to identify, discuss, and solve problems in a timely manner. Moreover, by carefully defining, sequencing, and weighting the targets at the plant and individual shop-floor levels, the company keeps frontline workers focused on the underlying factors that influence the efficiency of the process or activity at hand. This approach also ensures that these workers’ specific areas contribute to the plant’s big-picture energy-efficiency goals.

Meanwhile, at the corporate level, the company created a new organization, headed by a group vice president, that is responsible for energy efficiency. Assistant managers in each of the company’s plants work closely with specialists in the most energy-intensive divisions to monitor progress and suggest improvements. Some of these ideas have come from the shop floor, where workers now have a much clearer idea of how their actions influence energy use. Collaboration is also improved. As one vice department manager put it, “We have established much closer communication and cooperation between departments and plants along the energy value chain.”

The initial wave of results was encouraging, and changes continue to be rolled out at the flagship and other plants. After the first year, the flagship had exceeded its overall target, lowering its energy consumption by 12 percent and saving some 200 million renminbi (about $32 million). A second wave of energy-efficiency improvements, under way now, is expected to generate additional savings. Subsequent benchmarking found that the flagship plant is poised to become one of the world’s ten most energy-efficient facilities of its kind—a goal the company’s leaders expect to achieve in the near future. They now see energy efficiency as the biggest lever for boosting profits. Indeed, it is expected to contribute a majority of the operational-improvement gains the company has identified this year across its whole network of plants. These gains are projected to exceed those achieved at the flagship plant by more than a factor of ten.

Steve Chen and Maxine Fu  are consultants in McKinsey's Shanghai office; Arthur Wang is a principal in the Hong Kong office.

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