Regional Emission Reduction Research Articles

Structural Characteristics of Expressway Carbon Emission Correlation Network and Its Influencing Factors: A Case Study in Guangdong Province

Understanding the spatial correlation of transportation carbon emissions and their influencing factors is significant in achieving an overall regional carbon emission reduction. This study analyzed the structure characteristics of the expressway carbon emission correlation network in Guangdong Province and examined its influencing factors with intercity expressway traffic flow data using social network analysis (SNA). The findings indicate that the correlation network of expressway carbon emissions in Guangdong Province exhibited a “core-edge” spatial pattern. The overall network demonstrated strong cohesion and stability, and a significant difference existed between the passenger vehicle and freight vehicle carbon emission networks. The positions and roles of different cities varied within the carbon emission network, with the Pearl River Delta (PRD) cities being in a dominant position in the carbon network. Cities such as Guangzhou, Foshan, and Dongguan play the role of “bridges” in the carbon network. The expansion of differences in GDP per capita, industrial structure, technological level, and transportation intensity facilitates the formation of a carbon emission network. At the same time, geographical distance between cities and policy factors inhibit them. This study provides references for developing regional collaborative carbon emission governance programs.

The Impact of High-Standard Scenic Areas Construction on County-Level Carbon Emissions and Its Spatial Spillover Effects: Evidence from a Quasi-Natural Experiment

This study investigated for the first time the tourism–carbon emissions nexus based on the destination construction perspective, using the China’s national scenic areas (CNSA) construction as a vehicle for concretization. A multi-source county panel dataset of 29,628 samples was constructed. The staggered Difference-in-Differences (DID) model and spatial DID model were further formulated. The findings show that: (1) the CNSA resulted in a 0.1024% reduction in carbon emission intensity (CEI) in treatment counties relative to non-treatment counties, and although the effect exhibits a delay, it persists and intensifies over time; (2) our heterogeneity results indicate that the inhibiting effect is significantly more pronounced in the western, eastern, and county subsamples; and (3) the spatial DID analysis reveals that the CNSA exerts a negative spatial spillover effect on CEI. This work enhances comprehension of the tourism–carbon emissions nexus, with implications for advancing regional carbon emission reduction policy strategies.

Boosting Electric Vehicle Adoption with PCT Policy: Insights from China's Private Transportation

Personal Carbon Trading (PCT) motivates low-carbon consumption and travel by rewarding sustainable behaviors. This study examines the impact of PCT policies on electric vehicle (EV) adoption in China's private transportation sector. Using a Stackelberg game model involving the government, EV manufacturers, and consumers, it finds that PCT policies can boost EV adoption rates by 2.29% to 14.3%. Each addition of 100,000 new EVs could reduce carbon emissions by 30,000 tons. Effective PCT policies require an independent carbon pricing system and should be implemented where infrastructure and public willingness to pay are high. Both allocation methods are conducive to the promotion of EVs: one facilitates the government in setting regional emission reduction plans, while the other ensures that the government does not incur policy-related expenditures, and different allocation methods should be chosen according to the time and circumstances in different periods of EV market development.

Contrasting Domestic and Global Impacts of Emission Reductions in China on Tropospheric Ozone

AbstractSustained near‐surface ozone pollution despite stringent emission controls in China has drawn wide attention. However, how such rapid regional emission reductions affect global tropospheric ozone remains unexplored. Here, using an ensemble of measurements and global model simulations, we find Chinese emission reductions during 2013–2020 have increased domestic surface ozone; particularly in North China, they have decreased the global tropospheric ozone burden by 2.7 Tg (54% of China's anthropogenic contribution and 7% of global anthropogenic contribution in 2019) and ozone radiative forcing by 8.8 mW m−2 (64% of China's anthropogenic contribution and 9% of global anthropogenic contribution in 2019). Nitrogen oxides (NOx) associated nonlinear chemistry and height‐dependent atmospheric transport patterns primarily cause the contrasting impacts. Future Chinese NOx emission controls would continue to decrease global tropospheric ozone, while regional surface ozone may still increase unless NOx reductions exceed ∼53% of the 2019 level. Our results reveal the substantial benefit of regional emission reductions in China on global tropospheric ozone mitigation.

Response Mechanism and Evolution Trend of Carbon Effect in the Farmland Ecosystem of the Middle and Lower Reaches of the Yangtze River

The farmland system in the global terrestrial ecosystem has dual attributes as both a carbon source and a carbon sink, playing a crucial role in controlling carbon emissions and mitigating global warming. Using carbon source and sink accounting of farmland ecosystems, we applied methods such as standard deviation ellipse, Tapio decoupling theory, and Markov chain to analyze the spatiotemporal changes, response mechanisms, and evolutionary trends of regional carbon effects. The results indicated that from 2011 to 2021, the farmland ecosystem in the middle and lower reaches of the Yangtze River consistently acted as a carbon sink. However, the net carbon sink showed slight fluctuations and significant spatial differences. The migration range of the net carbon sink center in the farmland ecosystem of the middle and lower reaches of the Yangtze River was relatively small, ranging from 115.52 to 115.77° E and 30.14 to 30.27° N. The decomposition of the Tapio decoupling index between the net carbon sink of the farmland ecosystem and agricultural output value showed the order of effects on their coupling relationship as follows: agricultural mechanization level > agricultural mechanization efficiency > agricultural output value > planting scale. The probability of maintaining the original state of net carbon sink in various cities in the middle and lower reaches of the Yangtze River (over 77%) was much higher than the probability of transfer, making it difficult to achieve a leapfrog growth in net carbon sink. The net carbon sink at the city scale exhibits the Matthew effect and spatial spillover effect. The above research results clarify the spatiotemporal changes in carbon effects in agricultural production at multiple levels, including city, province, and region. They also provide a theoretical basis for formulating differentiated regional emission reduction and sink enhancement strategies in the middle and lower reaches of the Yangtze River, promoting the rapid development of low-carbon agriculture in China.

Carbon emission prediction in a region of Hainan Province based on improved STIRPAT model.

In 2020, China pledged carbon reduction targets at the United Nations: peaking emissions by 2030 and achieving carbon neutrality by 2060. Research and prediction of regional carbon emissions are crucial for achieving these dual carbon targets across China. This study aims to construct an indicator system for regional carbon emissions and utilize it for forecasting. Analyzing carbon emission data from a specific area in Hainan Province from 2010 to 2020, we established an indicator system. Using the interpretable SHAP model, we assessed indicator importance and trends. Employing an improved STIRPAT model with partial least squares regression to address multicollinearity among influencing factors, we developed a carbon emission prediction model. Based on this, we forecasted carbon emissions from 2021 to 2060 in the specified area under three scenarios: natural, baseline, and ambitious. The results show that the growth of resident population and per capita GDP has the most significant promoting effect on carbon emissions in the region while optimizing industrial structure, energy consumption structure, and reducing energy intensity will inhibit carbon emissions. The prediction results indicate that in the natural scenario, regional carbon emissions will peak in 2035, and achieving carbon neutrality by 2060 is not feasible, while the baseline scenario and ambitious scenario can achieve the dual carbon targets on time or even earlier. The research results of this article provide a reference method for predicting carbon emissions in other regions and a guide for future regional emission reduction.

Analysis of Spatiotemporal Changes in Energy Consumption Carbon Emissions at District and County Levels Based on Nighttime Light Data—A Case Study of Jiangsu Province in China

Approximately 86% of the total carbon emissions are generated by energy consumption, and the study of the variation of energy consumption carbon emissions (ECCE) is of vital significance to regional sustainable development and energy conservation. Currently, carbon emissions accounting mainly focuses on large and medium-scale statistics, but at smaller scales (district and county level), it still remains unclear. Due to the high correlation between nighttime light (NTL) data and ECCE, this study combines “energy inventory statistics” with NTL data to estimate ECCE at smaller scales. First, we obtained city-level statistics on ECCE and corrected the NTL data by applying the VANUI index to the original NTL data from NPP-VIIRS. Second, an analysis was conducted on the correlation between the two variables, and a model was created to fit the relationship between them. Under the assumption that ECCE will be consistent within a given region, we utilized the model to estimate ECCE in districts and counties, eventually obtaining correct results at the county-level. We estimated the ECCE in each district and county of Jiangsu Province from 2013 to 2022 using the above-proposed approach, and we examined the variations in these emissions both spatially and temporally across the districts and counties. The results revealed a significant degree of correlation between the two variables, with the R2 of the fitting models exceeding 0.8. Furthermore, ECCE in Jiangsu Province fluctuated upward during this period, with clear regional clustering characteristics. The study’s conclusions provide information about how carbon emissions from small-scale energy use are estimated. They also serve as a foundation for the creation of regional energy conservation and emission reduction policies, as well as a small-scale assessment of the present state.

Spatial pattern of forest aboveground biomass and its environmental influencing factors in Qinling-Daba Mountains, central China

Accurate estimation of forest aboveground biomass (AGB) is crucial for understanding and managing forest ecosystems in the context of global environmental change, and also provides a scientific basis for national and regional ecological planning and carbon emission reduction policies. In order to investigate the regional pattern of forest AGB and its influencing factors in central China, a total of 469 sample plots were measured along four sample transects and on six mountains in field survey. The results showed that: 1) Two longitudinal distribution patterns of forest AGB were found, and one was that the AGB in the Qinling Mountains and the Daba Moutains gradually decreased from east to west, and the other was that the AGB in the areas between the two mountains gradually increased from east to west. The latitudinal distribution pattern of the forest AGB showed an "increasing–decreasing-increasing–decreasing" trend from south to north. The altitudinal distribution pattern showed a "first increasing-then decreasing" pattern with increasing altitude. 2) The influence of each factor on the spatial pattern of forest AGB was manifested as temperature > precipitation > HAI > topography, indicating that the spatial pattern of forest AGB in central China was the result of the interaction of climate, human activities and natural factors.

Cross-scale characteristics and emission reduction pathways of trade-embodied carbon flows in the global supply Chains

In the context of the 1.5 °C warming target, there is a growing awareness of the increasingly frequent carbon flows between subregions. However, understanding the characteristics and pathways of cross-scale carbon flows between subregions and regions remains elusive and impedes achieving emission reduction targets. This study employs a nested multi-regional input–output model and the structural path analysis method, establishing the characteristics of the carbon emission transfer network within global supply chains and discerning the crucial structural pathways and nodes for carbon emissions. The study found that (1) in 36 major countries, the embodied carbon flows are predominantly influenced by the developed countries around China in Asia and the Pacific. (2) In 30 subregions, coastal subregions with large-scale foreign trade and energy-intensive subregions in China's central and western parts contribute significantly to the larger embodied carbon outflow due to exports. (3) “Manufacture of non-metallic mineral products (S09) → construction (S19)” and “manufacture and processing of metals (S10) → construction (S19)” are the two key pathways of paramount significance of embodied carbon emissions. (4) “R25 South Korea” has the highest number of key pathways and the greatest embodied carbon emissions from critical paths, followed by “R27 India” and “R22 United States.” These findings offer valuable insights for the development of regional emission reduction policies.

A study on the spatial correlation network structure and its influencing factors of coupling coordination between FDI flow network and carbon transfer network in the belt and road initiative countries

Clarifying the spatial correlation characteristics and influencing factors of coupling coordination between Foreign Direct Investment (FDI) flow networks and carbon transfer networks in countries along the Belt and Road Initiative is of utmost importance for the formulation of regional carbon governance strategies and the establishment of a high-quality Green Silk Road. This study used a comprehensive approach combining social network analysis and coupling coordination model to measure the coupling coordination degree of FDI flow networks and carbon transfer networks of 67 Belt and Road countries from 2010 to 2016. In addition, a modified gravity model is used to characterize the spatial correlation network structure of coupling coordination between the two networks, and the QAP regression analysis method is applied to investigate the factors influencing the spatial association network. The results are as follows: 1) The spatial correlation network of coupling coordination between the two networks has good accessibility and relatively high overall network stability. 2) Countries such as Qatar and the United Arab Emirates occupy central positions in the network, while Bahrain and Jordan are positioned on the periphery of the network. 3) The spatial correlation network can be divided into three sectors: net outflow, net inflow, and bidirectional overflow sectors. 4) Spatial adjacency, bilateral investment treaties, economic development, and institutional quality have significant positive effects on the spatial association network, while the industrial structure and the level of infrastructure development have a significant negative impact. This study proposes an indicator system for the coupling coordination between FDI flow networks and carbon transfer networks. The aim is to investigate the coupling coordination relationship between FDI flow networks and carbon emission transfer networks in countries along the Belt and Road Initiative, providing important guidance for the formulation of regional cooperative carbon emission reduction strategies in other regions.

WITHDRAWN: The impact of renewable energy development on regional carbon emission reduction: Based on the spatio-temporal analysis of 30 provinces in China

The development of renewable energy has become an important means for the world to cope with climate change, ensure energy security and protect the ecological environment. Using the panel data of 30 provinces in China from 2013 to 2021, this paper used the mediating effect model and the spatial Durbin model (SDM) to explore the mechanism and spatial effects of renewable energy development on carbon emission reduction. The results show that: (1) The results of mechanism analysis show that renewable energy development reduces carbon intensity by improving energy structure, promoting industrial structure optimization and industrial structure upgrading. (2) Renewable energy development can not only reduce the local carbon intensity, but also have a positive spillover effect on the carbon intensity of neighboring regions. (3) Further analysis shows that renewable energy development has a dynamic effect on carbon emissions. The heterogeneity analysis shows that compared with the Yellow River basin, renewable energy development significantly influences carbon emission reduction in the Yangtze River Economic Belt region. Energy-rich areas fall into the "resource curse", which makes renewable energy development’s carbon emission reduction effect not significant. This paper has certain reference significance for promoting reasonable decomposition between regions and formulating renewable energy development policies.

High-resolution carbon emission mapping and spatial-temporal analysis based on multi-source geographic data: A case study in Xi'an City, China

Cities, contributing over 70% of global emissions, are key areas for climate change mitigation. Heterogeneity within cities determines the need for spatialized urban emissions reduction policies. However, few studies have attempted to characterize the spatial distribution of carbon emissions at the urban scale. To address this issue, a novel mapping method was proposed, using Xi'an as an example to explore the spatial distribution of carbon emissions at the city scale. Firstly, multiple geospatial open-source data, such as point of interest (POI), road networks, and land use, were utilized to identify the locations of emission sources. High-resolution carbon emission distributions were then mapped by allocating emissions based on the Intergovernmental Panel on Climate Change (IPCC) methodology. The study employed Global Moran's I to analyze the changes in spatial heterogeneity at different resolutions. Additionally, the Local Indicators of Spatial Association index (LISA) and Standard Deviation Ellipses (SDE) were adopted to examine the spatiotemporal characteristics of carbon emissions in Xi'an. The results show that carbon emissions at Xi'an City rises from 45.112 million tons to 72.701 million tons between 2010 and 2021. The construction of multi-scale carbon emissions spatial distributions, with a resolution of up to 30 m, allowed for a more detailed characterization of carbon emissions, especially in urban fringe areas. In addition, the results indicate that urban carbon emissions exhibit the strongest spatial autocorrelation at a resolution of 350 m. The study can provide a reference for the development of regional carbon emission reduction policies and spatial planning. In addition, the proposed spatialized method of city carbon emissions depends on open-source data, which allows it to have the potential for application in other cities.

Spillover effects of structure-adjustment pollution reduction measures in China's iron and steel industry

The iron and steel industry (ISI) is a significant source of sulfur dioxide and particulate matter pollution in China. Existing research on regional environmental regulation or ISI emission reduction strategies tends to overlook spillover effects and the enterprise perspective. During the heating season, production limitations in ISI are potential policy measures for achieving structural emission reductions in heavily polluted cities in China's Jing-Jin-Ji and surrounding regions. We adopt a bottom-up modeling approach, incorporating effective production time to describe enterprise behavior and establishing a quantitative trade model based on trade theory. By modeling three types of production restriction policies outlined in policy documents, we evaluate the emission reduction effects of structure-adjustment measures using the example of reduced effective production time for steel-producing enterprises in the air pollution transmission channel in the Beijing-Tianjin-Hebei area. The results indicate the following: (1) Reducing the effective production time of ISI enterprises can help decrease domestic production value and total factor productivity in pollution-intensive industries, including but not limited to ISI. It also leads to reduced emissions of various pollutants in the implementation regions. (2) Due to interprovincial trade and input-output linkages, structural reduction measures in certain regions have implications for almost all other provinces' industrial structures. Differences in initial industrial structures, factor endowments, and geographical locations contribute to varying directions and magnitudes of industrial structural changes. Pollution-intensive industries' share tends to increase higher in less developed regions. (3) Our estimated pollution reduction is smaller compared to the literature evaluating clean air policies in similar regions using top-down strategies. This discrepancy arises because we analyze a single policy tool rather than modeling industry-wide emission fluctuations from the top down. Additionally, our modeling approach allows us to examine dynamic changes in comparative advantages. The increase in production scale for certain industries in policy-affected regions partially offsets the decline in pollution emissions. These findings enhance our understanding of structure-adjustment reduction measures' role and highlight their potential advantages and limitations.

Spatial network analysis and driving forces of urban carbon emission performance: Insights from Guangdong Province

As the primary contributor to carbon emissions, how cities enhance their carbon emission performance and mitigate emissions is crucial for achieving low-carbon urban environments in China. However, existing research often overlooks the spatial interconnectedness of carbon emission performance, neglecting reciprocal influences among cities. This study examines the network structure of carbon emission performance among Guangdong's cities from 1997 to 2019, using a super-efficient SBM model and social network analysis, and measures spatial impacts of network factors with the spatial Durbin model. Findings reveal that: (1) The overall network of carbon emission performance is relatively loose with minimal changes in connectivity and efficiency but shows significant local clustering. (2) Shenzhen, Guangzhou, and Zhuhai have high centrality, dominating carbon emission performance resources and acting as key transmission nodes, while most other regions have low centrality, indicating network polarization and potential vulnerabilities. (3) Enhancing a region's centrality, economic development, industrial structure, openness, and attraction of talent and technology can boost local carbon emission performance, but may also lead to the displacement of emissions to neighboring areas and outflow of low-carbon and innovative elements, negatively affecting surrounding regions through spatial spillover effects. This research advances regional carbon emission reduction strategies by highlighting the interplay between spatial networks and carbon emission performance, fostering synergies in reduction efforts.

WITHDRAWN: Short-term Load Forecasting Method Based on LSTM Optimized by the Hybrid Dung Bettle Optimization Algorithm

Forecasting electricity load is a crucial responsibility for the power system. Precise short-term power load forecasting (STLF) is a valuable tool for regional production planning, energy conservation, emission reduction, and grid coordination and dispatch. This study suggests a model for STLF that makes use of an optimized Long and Short-Term Memory (LSTM) neural network through the Hybrid Dung Beetle Optimization algorithm (HDBO-LSTM). Firstly, the Hybrid Dung Beetle Optimization (HDBO) algorithm is used to optimize the LSTM network's hyperparameters to address the problem that the LSTM network's random hyperparameter selection will materially influence the accuracy of load forecasting. Secondly, to address the problems that the original Dung Beetle Optimization (DBO) algorithm has poor global exploration ability and is easy to falls into local optimization, multiple improvement strategies are proposed to enhance the DBO algorithm, and the HDBO algorithm, which has a stronger searching ability and faster convergence speed, is proposed. By comparing with several algorithms on 10 benchmark functions, HDBO shows better search performance. Finally, the HDBO-LSTM load forecasting model is developed and evaluated using the Electrotechnical Mathematical Modeling Competition's power load dataset. The outcomes show that HDBO-LSTM outperforms the other comparison models in terms of accuracy and predicting efficacy.

Study on Interprovincial Equity and the Decoupling of Carbon Emissions in the Construction Industry—A Case Study in China

Interprovincial disparities in carbon emissions from the construction industry (CECI) are an important challenge for future emissions reductions. Based on the CECI data of 30 provinces in China from 2010 to 2020, the interprovincial equity and decoupling of CECI were examined in this study. The conclusions are as follows: (1): The main CECI growth regions were the eastern Bohai Rim region and the Pearl River Delta region. Heilongjiang basically achieved a CECI carbon peak in 2016. (2) The three northeastern provinces and western provinces were the main high–high and low–low aggregation areas, respectively. The greatest degree of inequality was found in the western provinces. The inequality between the eastern and western areas was the largest, while the inequality between the central and western areas was the smallest. The inequality in CECI in the north–south region decreased year by year. (3) The decoupling status of Shandong and some western provinces has significantly worsened, while the decoupling status of Sichuan, Yunnan, and some eastern developed provinces has been improving. These conclusions will contribute to the improvement of regional emission reduction strategies.

The Impact of Regional Carbon Emission Reduction on Corporate ESG Performance in China

The integrated planning of central and local emission reduction tasks is crucial for achieving sustainable economic development, and corporate ESG performance aligns with the principles of sustainable development, having become a prominent topic in academic research. This paper empirically investigates the impact of regional carbon emission reductions on the ESG performance of local enterprises from 2009 to 2021 using provincial carbon emission data from China. The findings indicate that regional carbon emission reductions significantly enhance the ESG performance of local firms. The underlying mechanism is that regional carbon emission reductions facilitate local enterprises obtaining green credit, attracting media coverage and green investors and thus improving ESG performance. Second, heterogeneity tests reveal that regional carbon emission reductions enhance the ESG performance of local firms more significantly in regions with stricter environmental regulations, within heavily polluted industries, and among less digitized enterprises. Finally, further analysis demonstrates that regional residents’ carbon emission reductions can enhance the ESG performance of local enterprises, with regional carbon emission reductions exerting a dual effect after improving ESG performance. The findings of this study provide valuable insights into the low-carbon development of various economic entities and the collaborative promotion of economic green transformation.

Source apportionment and formation of warm season ozone pollution in Chengdu based on CMAQ-ISAM

In this study, the WRF-CMAQ model integrated with BEM urban canopy model was used to simulate the concentrations of Ozone (O3) and its precursors, NOx and VOCs, in warm season of Chengdu, conduct source apportionment and formation analysis. The results show that the O3 in Chengdu exhibits a west-high/east-low spatial pattern, attributable to nearly 40% contribution from boundary sources representing the transport role of the Sichuan Basin, regional sources from districts emitting high precursor concentrations, and increasing biogenic contributions from western areas due to rising BVOCs emissions during the warm season. NOx from traffic and VOCs from industrial sources, both prevalent in Chengdu's high urban density areas, chemically react to form O3, making these sectors primary contributors to O3. NOx photochemical reactions producing O3 occur at 150 m–2500 m with peak generation rates of 10 μg/(m3·hr). Ground-level NO titration removal is most significant during heavy traffic (14:00–21:00), ranging from −70 to −200 μg/(m3·hr). O3 is replenished through similar rates of daytime vertical diffusion and nighttime horizontal advection, correlating with urban density across regions. Controlling Chengdu's warm season O3 requires focusing on long-distance external transport and regional precursor emission reductions, with strategies tailored to local urban characteristics.

Will the Low-carbon City Pilot Policy Achieve Regional Emission Reduction?

In order to address climate change and promote green and low-carbon development, the Chinese government has implemented a pilot policy for low-carbon cities since 2010 and has continuously expanded the scope of the pilots. Taking the second batch of low-carbon pilot policies as an example, this paper uses difference in difference method to study the impact of low-carbon pilot policies on reducing urban carbon emission intensity based on the panel data of prefecture level cities from 2006 to 2019. It is found that the construction of low-carbon cities has a positive effect on reducing carbon emissions,and this conclusion is still valid under the robustness test. Based on this conclusion,this paper argues that China should further promote low-carbon pilot policies and actively explore the development model of low-carbon cities, especially to improve the livable and green urban environment.

The Impact of Renewable Energy Development on Regional Carbon Emission Reduction: Based on the Spatio-Temporal Analysis of 30 Provinces in China.

The development of renewable energy has become an important means for the world to cope with climate change, ensure energy security, and protect the ecological environment. Using the panel data of 30 provinces in China from 2013 to 2021, this study used the mediating effect model and the spatial Durbin model (SDM) to explore the mechanism and spatial effects of renewable energy development on China's regional carbon emission reduction. The results show that: (1) Renewable energy development can help to reduce carbon emission intensity. (2) The results of mechanism analysis show that renewable energy development reduces carbon intensity by improving energy structure, promoting industrial structure optimization, and industrial structure upgrading. (3) The development of renewable energy can not only reduce the local carbon intensity but also have a positive spillover effect on the carbon intensity of neighboring regions. (4) Further analysis shows that the long-term effect of renewable energy development on carbon emissions is greater than the short-term effect. At the same time, the heterogeneity analysis shows that compared with the Yellow River basin, the development of renewable energy has a significant carbon emission reduction effect in the Yangtze River Economic Belt region. Energy-rich areas fall into the "resource curse", which makes the carbon emission reduction effect of renewable energy development not significant. This paper has certain reference significance for promoting reasonable decomposition between regions and formulating renewable energy development policies.