Logarithmic Mean Divisia Index Research Articles

Unveiling the dynamic flows and spatial inequalities arising from agricultural methane and nitrous oxide emissions

Tracing the spatial transfer and heterogeneity of agricultural methane (CH4) and nitrous oxide (N2O) emissions in China is a prerequisite for the sustainable transformation of agricultural systems. In this study, we established a research framework for evaluating agricultural CH4 and N2O flows and convergence. Using this framework, we established an inventory of China's agricultural CH4 and N2O emissions calculated according to the IPCC inventory guidelines, built a food trade model to simulate the spatial transfer, and revealed the regional differences. Finally, we analyzed the influence mechanism by combining extended Kaya identity and the logarithmic mean divisia index (LMDI) model. We found that inter-regional transfer of agricultural CH4 and N2O emissions in China have intensified, increasing from 56.14 % of total transfers in 2000 to 67.28 % in 2019. The spatial inequalities of agricultural CH4 and N2O increased, and emission intensity varied more within regions than between regions, with per capita emissions showing a club convergence with “intragroup convergence and intergroup divergence”. Although the contribution of agricultural CH4 and N2O emissions varies across provinces, controlling emissions intensity and land use intensity while maintaining GDP per capita is the key to emission mitigation. Our study provides theoretical support for prioritizing policies to mitigate agricultural CH4 and N2O emissions.

Analysis of Energy-Related-CO2-Emission Decoupling from Economic Expansion and CO2 Drivers: The Tianjin Experience in China

Cities are key areas for carbon control and reduction. The study of the decoupling between CO2 emissions and gross domestic product (GDP) and the drivers of CO2 emissions in cities facilitates the reduction of CO2 emissions to safeguard the development of the economy. This paper first calculates the CO2 emissions in Tianjin, China, from 2005 to 2022, then uses the Tapio decoupling index to quantify the decoupling status, and, finally, explores the energy-CO2-emission drivers through the Logarithmic Mean Divisia Index (LMDI) model. The findings indicate that (1) the decrease in CO2 emissions from industrial products and transport is the main reason for the decline. (2) During the period under investigation, the predominant condition observed was a state of weak decoupling. (3) Given the economic-output effect is the primary and substantial driver of energy CO2 emissions, it is essential to harmonize the interplay between economic-development approach and CO2 emissions to foster sustainable development in Tianjin. The industrial structure plays the most critical role in hindering the reduction of CO2 emissions; therefore, optimizing industrial structure can help achieve carbon reduction and control targets. These findings enrich the study of CO2 emission factors and can also interest urban policymakers.

Long-term monitoring of citizen science: driving factors and pandemic impacts

ABSTRACT In recent years, the importance of citizen science in biodiversity monitoring has become increasingly evident. Citizen science provides valuable data to understand the dynamic changes in biodiversity across different cities. Although long-term monitoring is crucial for understanding these dynamic changes, previous research has primarily focused on short-term data analysis. Therefore, this study analysed data from the iNaturalist platform for 11 Japanese cities from 2016 to 2023, focusing on the driving factors of observation changes and the impact of the COVID-19 pandemic using the Logarithmic Mean Divisia Index (LMDI) method. The results revealed that the population (total number of active users) metric had the greatest impact, followed by intensity (the number of observations per active day), frequency (active days per active user), and structure (proportion of regular users). To ensure data continuity and reliability, optimizing user structure by increasing the proportion of regular users is essential. Although observation volumes fluctuated during the pandemic, they rebounded significantly post-pandemic, demonstrating the resilience, and adaptability of citizen science projects. The iNaturalist platform provides a stable foundation for long-term data collection, aiding in the comprehensive understanding of biodiversity dynamics. Our study also highlighted differences in citizen science participation and biodiversity monitoring across cities, indicating the need for tailored biodiversity conservation policies. To effectively address similar crises in the future, developing contingency plans is recommended to provide continuous support for biodiversity conservation and public engagement in environmental management.

Key effects contributing to changes in energy imports in the EU-27 between 2000 and 2020: A decomposition analysis based on the Sankey diagram

The aim of this paper is to analyse the key effects contributing to changes in energy imports in the European Union (EU-27) in the period 2000–2020. Using the Logarithmic Mean Divisia Index (LMDI), the analysis examines the effect of changes in six factors—energy structure, energy dependence, energy transformation efficiency, energy yield after transformation, energy efficiency and activity—on imports of oil, natural gas and other sources of energy. The results of the analysis reveal that the decarbonisation process has fostered the abandonment of the most polluting fossil fuel sources; however, there has been an increase in energy dependence on less polluting sources that are not produced domestically. In contrast, there has been an opportunity for change through improved energy efficiency, which has made it possible to reduce energy requirements per unit produced. In short, in order to achieve a sustainable and secure energy future, it is crucial to implement policies and actions that promote both the diversification of the energy mix—particularly renewable energies—and efficiency in consumption. Doing so will enable countries to move towards true decarbonisation and minimise vulnerability in their energy supply.

Agricultural carbon emissions in China: measurement, spatiotemporal evolution, and influencing factors analysis

IntroductionThe agricultural sector is the second largest emitter of greenhouse gases, accounting for 23% of global anthropogenic carbon emissions. Analysis of the basic state of carbon emissions from China's agriculture is helpful to achieve carbon reduction targets.MethodsAgricultural carbon emissions were calculated using the emission factor method, based on data from the China Rural Statistical Yearbook and various provincial statistical yearbooks. To analyze spatial patterns, the standard deviation ellipse method and the center of gravity migration model were employed, uncovering the migration path of agricultural carbon emissions. Regional disparities and the driving factors of agricultural carbon emissions were further examined using the Theil index and the Logarithmic Mean Divisia Index (LMDI) model.ResultsThe analysis indicated that the emissions center has gradually shifted towards the central and western regions, reflecting changes in agricultural production activity areas. Intraregional differences are the primary contributors to the imbalance in agricultural carbon emissions, with pronounced disparities in grain production and consumption balance regions. Key influencing factors include agricultural production efficiency, adjustments in agricultural industrial structure, economic structure and output, and urbanization levels. The economic output effect and urbanization effect are identified as the main drivers of increased carbon emissions, while declining production efficiency has hindered emission reduction efforts.ConclusionThe findings provide valuable insights for regional management and policymaking in China's agricultural sector, highlighting the need to enhance production efficiency and optimize agricultural structure to reduce emissions.

Investigating carbon peak prediction scenarios and implementation strategies in higher education institutions: A case study of Shandong Jianzhu university

The potential for energy conservation and emission reduction in Chinese universities is significant. The exploration of scenarios for predicting carbon peaking and identifying implementation pathways can offer valuable insights and demonstrations for achieving dual carbon goals. This paper develops a research methodology for carbon peaking in universities, with a focus on four main aspects: carbon emission accounting, the analysis of influencing factors, carbon emission prediction, and implementation pathways for carbon peaking. By utilizing the campus of Shandong Jianzhu University as a case study, the paper practically demonstrates the prediction of carbon peaking scenarios and offers technical solutions for universities. The results demonstrate that: (1) Using the Logarithmic Mean Divisia Index (LMDI) decomposition method to investigate the influencing factors of carbon emissions in universities, we confirm that the energy structure effect, economic effect, and population effect have a promoting impact on the increase of carbon emissions at Shandong Jianzhu University, while the energy intensity effect exhibits a significant inhibitory effect. (2) By employing Partial Least Squares (PLS) regression analysis of annual carbon emission statistics from universities, we construct a STIRPAT model with high predictive accuracy for carbon emission regression. The carbon emission prediction model for Shandong Jianzhu University is: lnC^t=-25.899+3.571lnPt+0.966lnARt+0.716lnTMt-0.279lnTEt (3) Both the low-carbon scenario and the enhanced scenario for Shandong Jianzhu University are capable of achieving carbon peaking by 2027. From the perspectives of energy-saving renovation of existing building envelopes, improving the efficiency of lighting systems, utilizing solar photovoltaic buildings, and electrifying cooking energy, implementation pathways for carbon peaking under low-carbon and enhanced scenarios are delineated.

Spatiotemporal Evolution and Driving Factors of Land Use Carbon Emissions in Jiangxi Province, China

Analyzing the spatiotemporal changes and influencing factors of carbon emissions generated by land use is of great importance for improving land use structure and promoting regional low-carbon economic development. This study, based on remote sensing and statistical yearbook data from 1995 to 2020, calculated the carbon emissions from land use in Jiangxi Province, China. Multiple spatial analysis methods and the logarithmic mean Divisia index were used to elucidate the spatiotemporal evolution and driving factors of carbon emissions, and the findings revealed the following: (1) The spatiotemporal changes in land use in Jiangxi Province during 1995–2020 were substantial as forest land accounted for 65% of the entire land area, while construction land increased by 98.1%. Cultivated land decreased the most, followed by forest land. (2) There was a fourfold rise in carbon emissions in Jiangxi Province, driven primarily by construction land, and northern areas produced higher carbon emissions compared with central and southern regions. Forest land was the main carbon sink. (3) Economic development (257.36%) and the impact of the proportion of construction land (211.31%) were the primary factors contributing to the increase in carbon emissions from land use, while other factors had inhibitory effects. This study transformed the macroscale low-carbon development strategy of cities into targeted local policies, and the research theories and methods adopted could provide scientific reference for other regions in urgent need of carbon reduction worldwide.

Decoupling analysis of economic development and human well-being: A case study of the Qinghai-Xizang Plateau, China.

Clarifying the relationship between economic development and human well-being is conducive to promoting high-quality economic development. This study focused on 16 prefecture-level cities in the Qinghai-Xizang Plateau region. The critic weighting method assessed the 2007-2018 human well-being index (HWI). The Tapio decoupling model allowed the study of the human well-being decoupling state. Finally, the drivers of decoupling between economic development and human well-being were analyzed using the Logarithmic Mean Divisia Index method. The results indicated that (1) almost all cities in the study region had an upward 2007-2018 HWI trend, but there were significant differences in growth magnitude and change trend. (2) Economic development and human well-being in the study region in 2007-2018 had expansion negative decoupling, thus, human well-being increased with economic growth, but not as fast as gross domestic product. 9 cities in 2007 showed weak decoupling, expansion connection, and expansion negative decoupling, increasing to 13 cities by 2018, indicating that human well-being development gradually improved from 2007 to 2018. (3) For most cities, the economic scale effect was the most influential factor in the decoupling of economic development and human well-being. Therefore, this study provided policy recommendations for decoupling economic development and human well-being.

Tackling carbon peak and carbon neutrality challenges: A method with long-range energy alternatives planning system and Logarithmic Mean Divisia Index Integration

China aims to carbon peak emissions before 2030 and achieve carbon neutrality by 2060 in response to climate change challenges. Studying the feasibility of this goal, identifying potential gaps, and formulating corresponding emission reduction pathways are urgent issues that need to be addressed. This study creatively integrates Logarithmic Mean Divisia Index into Long-range Energy Alternatives Planning System to assess energy consumption and carbon emissions in Gansu Province from 2001 to 2060, historical and scenario analysis was conducted from a novel perspective. Results indicate that the economic development effect is the most significant driving factor for the emission growth of 131.54 million tons from 2001 to 2020, and its contribution rate is 176.31 %. The leading negative driving factor is the energy intensity effect, contributing at a rate of −62.00 %. Additionally, the energy structure and industrial structure effects also play minor negative roles. Scenario analysis suggests achieving a carbon peak by 2030 requires a 29.53 % reduction in energy intensity from the 2020 baseline, with electricity consumption must reach 46.37 % and clean energy generating 64 %. Carbon peak emissions are projected to be 220.37 million tons, with reduction of carbon emissions by 123.84 million tons by 2060.

Decoupling relationship between logistics growth and carbon emissions and driving factors in Chongqing: A novel decomposition framework

A comprehensive understanding of the logistics decoupling status and driving factors is of immense theoretical and practical importance for rationally formulating low-carbon logistics policies and accelerating the realization of high-quality development. Present study introduces the logarithmic mean Divisia index method (LMDI) decomposition technique into Tapio index model, extends traditional decoupling model, and establishes a new analytical framework for the decoupling relationship between logistics growth and carbon emissions. Using long-term data from Chongqing (1997–2021), this study investigated the Chongqing logistics decoupling relationship and driving factors. The study found the following: (1) Chongqing logistics carbon emissions show phased changes and face greater pressure to reduce emissions. (2) The decoupling status of logistics growth and carbon emissions is predominantly expansive and weak decoupling, with an overall evolutionary trend of “expansive decoupling–weak decoupling–strong decoupling. The average decoupling index in 2013–2021 was 0.5523, indicating a decreasing trend; however, there was still a large gap in realizing a strong decoupling goal. (3) The energy consumption intensity effect facilitates logistics carbon decoupling, the economic scale effect has a strong inhibiting effect, and the industrial structure effect and transportation intensity effect are staged. Finally, targeted policy recommendations are proposed to expedite logistics carbon emissions decoupling in Chongqing.

Influencing factors and predictions of carbon emissions for the chemical industry in China

As global warming increases the frequent occurrences of natural disasters, the reduction of carbon emissions has become an important issue around the world. The chemical industry is an important source of carbon emissions in China. The carbon emissions of the chemical industry are calculated from 2000 to 2019 by using the emission factor method. The logarithmic mean divisia index (LMDI) method is exploited to analyze the factors that influence carbon emissions, and the emissions variations are attributed to the contributions of carbon intensity, energy structure, energy intensity, industrial value-added rate, per capita industrial output value, and industrial scale. The results of decomposition show that per capita industrial output value is the main driving factor, and energy intensity is the main inhibiting factor of the chemical industry’s carbon emissions. In order to quantify the variation of carbon emissions, the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model is constructed and examined. Using the STIRPAT model, the basic scenario and energy intensity control scenario are set, and the carbon emissions are predicted, which shows that under a strict energy intensity control scenario, carbon emissions may reach a peak around 2031. The factors influencing the decomposition and prediction of carbon emissions should be helpful in reducing the carbon emissions of the chemical industry in China.

Decoupling effect and influencing factors of carbon emissions in China: Based on production, consumption, and income responsibilities

Carbon emissions at the provincial level are mostly calculated based on production responsibility, which ignores the carbon emission accounting perspectives of consumption and income responsibility. However, scientific accounting of carbon emissions from multiple perspectives is crucial to equitably distribute the responsibilities for reducing emissions. This study utilises an input–output model for multi-regional carbon emissions to calculate carbon emissions from production-, consumption- and income-based perspectives for exploring the variability in the responsibilities for reducing emissions at the provincial level. The Tapio decoupling coefficient is used to analyse the relationship between carbon emissions and economic growth in key provinces, and the Logarithmic Mean Divisia Index (LMDI) model is used to determine the factors promoting or inhibiting the decoupling of carbon emissions under different responsibility perspectives. The results show that, firstly, the national carbon emissions illustrate the following trend: production-based emissions > income-based emissions > consumption-based emissions. Secondly, provinces in strong decoupling states are distributed in a northeast–southwest direction. Carbon emissions and decoupling situations differ among varying responsibility perspectives due to interprovincial transfers of carbon emissions. Thus, a compensation mechanism should be established to promote a fair and orderly peaking in each province. Lastly, carbon emission intensity is a factor promoting decoupling in all three perspectives. In the meantime, output scale, economic development and factor input are the major inhibiting factors for the decoupling of production-, consumption- and income-based emissions, respectively.

Income-based environmental effects of family food consumption and the affordability towards healthy diets

Environmentally sustainable diets are attracting increasing attention, but the environmental impact of diets with different income residents is still poorly understood. Based on the environmental footprint method, we explored the environmental impact of food consumption in China from 2000 to 2020, and used the Logarithmic Mean Divisia Index (LMDI) model to investigate its driving factors. Furthermore, we constructed a multi-objective optimization model, incorporating the affordability indicator, to optimize a healthy diet pattern. The results show that: (1) The food diversity index (FDI) was directly proportional to income, and geographically, the FDIs of economically developed provinces were higher than that of economically developing provinces. (2) The environmental footprint caused by food consumption was gradually increasing, with 60 % of the carbon footprint of low-income residents coming from grain and pork. (3) Dietary affordability was a major factor in increasing environmental footprint. High-income residents had greater access to milk, meat, etc., which had a greater negative environmental impact. We put forward suggestions on dietary optimization and affordability for different income residents, with a view to achieving nutritional and healthy diets of Chinese while generating minimum resource and environmental load, and promoting the transformation of the agro-food system.

Measurement, spatial differences and driving effects of well-being levels in China.

As the largest developing country, China has accumulated enormous material wealth since its reform and opening-up policy. How to effectively evaluate the level of well-being in China has become a meaningful research endeavor. Using the entropy method, Dagum Gini coefficient and Logarithmic Mean Divisia Index (LMDI) decomposition methods, the study examines the spatial and temporal distribution characteristics, spatial differences and driving effects of provincial well-being levels from 2007 to 2020. The results of this study suggest that the level of well-being as a whole, as well as in the eastern, central and western regions increased significantly over the period, with an "east-to-west decreasing" distribution in China. In terms of the pattern of inter-provincial distribution, although the level of well-being in the central and western regions has improved at a faster rate, most provinces in the eastern region have always been among the leading teams on the path of livelihood development. There is still enormous room for improvement in the level of well-being in the central and western provinces. The overall differences in the development of well-being in China, as well as intra-regional and inter-regional differences among the three major regions, showed a narrowing trend. Intra-regional differences in the development of well-being are greatest in the western region, and inter-regional differences in the development of well-being are greatest in the eastern and western regions. Inter-regional differences are the main reason for the spatial differences in well-being among China's provinces. The combination of economic, social, ecological and technological effects has led to a gradual increase in the level of well-being over the sample period. Among them, economic, social and technological effects have a clear positive driving effect on the increase of well-being levels, while ecological effect have a certain negative driving influence.

Spatiotemporal drivers of agricultural non-point source pollution: A case study of the Huang-Huai-Hai Plain, China

Agricultural non-point source pollution (ANPSP) poses a severe threat to ecological environments, especially in China's major grain-producing regions. Despite the increasing attention, existing studies often overlook the spatial heterogeneity and driving mechanisms of ANPSP within different functional regions. This study addresses this research gap by constructing a bottom-up regional inventory of ANPSP for the Huang-Huai-Hai Plain (HHHP) and applying the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyse the spatio-temporal patterns of ANPSP from 2000 to 2020. Spatial econometric models were further applied to examine the spatial spillover effects of driving factors from the perspective of Major Function-oriented Zoning (MFZ). The results show that while ANPSP emissions in the HHHP have generally increased over the past two decades, a slight decrease has been observed since 2015. Grain yield capacity and cropping intensity were identified as the primary drivers of ANPSP growth, particularly in urbanised zones (UZs) and main agricultural production zones (MAPZs). The study also highlights significant spatial heterogeneity in the impact of driving factors on ANPSP across different MFZs, with marked differences in both the direct and spatial spillover effects of these factors. This underlines the need for differentiated environmental protection policies tailored to the functions and characteristics of each region. By integrating the LMDI decomposition method with spatial econometric models, this study offers a new framework for understanding the ANPSP dynamics within the context of MFZs, providing policymakers with valuable insights for designing effective, regionally coordinated governance strategies.

Change in the sustainability of regional agricultural systems: based on an emergy decomposition analysis

Abstract In the context of rapid population growth and limited arable land resources, the agricultural system has to provide enough food in a sustainable way. Regional agricultural systems have good consistency in agricultural practices, management decisions, social economy, and climate, which is of great significance in ensuring food security. In this study, emergy analysis and the logarithmic mean divisia index method were integrated to construct an evaluation framework from the dimensions of socio-economic environment, resource environment, climate environment, and ecological environment. Then we evaluated and analyzed the changes in agricultural system sustainability from 1990 to 2019 in the mainstream of Liaohe River Basin, a typical agricultural basin in China. The results showed that the Emergy sustainability index (ESI) decreased from 0.17 to 0.14, and factors Δ Pt/Gt (social and economic development level), Δ G/I (agricultural economic benefits), and Δ Gt/G (economic structure) from the socio-economic environment dimension had the greatest impact on changes in ESI. Moreover, society and economy affected the factors in the resource environmental dimensions through the allocation of policies and resources, which in turn directly affected ESI. The influence of factors from the climatic environment and ecological environment weakens as the ability to manage agricultural systems increases. The research provided a reference for the planning and management of sustaining agricultural systems at a regional scale.

Greenhouse gas emissions from Chinese livestock sector can be decreased by one third in 2030 by the improvement in management

China is one of the largest contributors to global greenhouse gas (GHG) emissions, and the livestock sector is a major source of non-CO2 GHG emissions. Mitigation of GHG emissions from the livestock sector is beneficial to the sustainable development of the livestock sector in China. This study investigated the provincial level of GHG emissions from the livestock sector between 2000 and 2020 in China, to determine the driving factors affecting the provincial-level GHG emissions from the livestock sector, based on the logarithmic mean Divisia index (LMDI) model, which took into account of technological progress, livestock structure, economic factor, and agricultural population. Moreover, a gray model GM (1, 1) was used to predict livestock GHG emissions in each province until 2030 in China. The results showed that the GHG of Chinese livestock sector was decreased from 195.1 million tons (MT) CO2e in 2000 to 157.2 MT CO2e in 2020. Henan, Shandong, and Hebei provinces were the main contributors to the reduction in Chinese livestock GHG emissions, with their livestock GHG emissions reduced by 60.1%, 53.5% and 45.5%, respectively, in 2020 as compared to 2000. The reduction in GHG emissions from the Chinese livestock sector can be attributed to two main factors: technological progress and the shrinking of the agricultural laborers. In contrast, the agricultural economic development model with high input and high emissions showed a negative impact on GHG emission reduction in China’s livestock sector. Furthermore, the different livestock structure in each province led to different GHG reduction effects on the livestock sector. Under the gray model GM (1,1), the GHG emissions of the livestock sector will be reduced by 33.7% in 2030 as compared with 2020 in China, and the efficiency factor will account for 76.6% of the positive effect of GHG reduction in 2030. The eastern coastal region will be the main contributor to the reduction of GHG emissions from the Chinese livestock sector in 2030. Moreover, recommendations (such as upgrading livestock management methods and promoting carbon emission mitigation industries) should be proposed for the environmentally sustainable development of the livestock sector in the future.

Decoupling Analysis of Economic Growth and Carbon Emissions in Xinjiang Based on Tapio and Logarithmic Mean Divisia Index Models

Decoupling Analysis of Economic Growth and Carbon Emissions in Xinjiang Based on Tapio and Logarithmic Mean Divisia Index Models

The Application and Evaluation of the LMDI Method in Building Carbon Emissions Analysis: A Comprehensive Review

The Logarithmic Mean Divisia Index (LMDI) method is widely applied in research on carbon emissions, urban energy consumption, and the building sector, and is useful for theoretical research and evaluation. The approach is especially beneficial for combating climate change and encouraging energy transitions. During the method’s development, there are opportunities to develop advanced formulas to improve the accuracy of studies, as indicated by past research, that have yet to be fully explored through experimentation. This study reviews previous research on the LMDI method in the context of building carbon emissions, offering a comprehensive overview of its application. It summarizes the technical foundations, applications, and evaluations of the LMDI method and analyzes the major research trends and common calculation methods used in the past 25 years in the LMDI-related field. Moreover, it reviews the use of the LMDI in the building sector, urban energy, and carbon emissions and discusses other methods, such as the Generalized Divisia Index Method (GDIM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) techniques. This study explores and compares the advantages and disadvantages of these methods and their use in the building sector to the LMDI. Finally, this paper concludes by highlighting future possibilities of the LMDI, suggesting how the LMDI can be integrated with other models for more comprehensive analysis. However, in current research, there is still a lack of an extensive study of the driving factors in low-carbon city development. The previous related studies often focused on single factors or specific domains without an interdisciplinary understanding of the interactions between factors. Moreover, traditional decomposition methods, such as the LMDI, face challenges in handling large-scale data and highly depend on data quality. Together with the estimation of kernel density and spatial correlation analysis, the enhanced LMDI method overcomes these drawbacks by offering a more comprehensive review of the drivers of energy usage and carbon emissions. Integrating machine learning and big data technologies can enhance data-processing capabilities and analytical accuracy, offering scientific policy recommendations and practical tools for low-carbon city development. Through particular case studies, this paper indicates the effectiveness of these approaches and proposes measures that include optimizing building design, enhancing energy efficiency, and refining energy-management procedures. These efforts aim to promote smart cities and achieve sustainable development goals.

Spatial-temporal evolution characteristics and driving factors analysis of regional energy supply and demand in China

Under the requirements of the Sustainable Development Goals, the study of China's energy development is an important reference for other countries to plan their energy policies. China is facing the problem of slow energy transformation processes and a continuously widening gap between supply and demand. Using panel data for 2012–2021, this study analyzes the characteristics of energy in China's 30 provinces, and clarifies the problems faced by each province. GM (1,1) model is employed to forecast the level of energy development from 2023 to 2030. The LMDI (Logarithmic mean Divisia index) model is utilized to analyze factors impacting energy demand in each province, according to which the design of energy development direction in each region is completed. The study finds that: (1) Overall, energy supply and demand in China are marked by “high in the west, low in the east” and “low in the west, high in the east,” respectively. The energy structure of each region is continuously optimized, but coal still has a high share. (2) The pace of clean energy development is satisfactory. However, natural gas's increase in share is relatively slight on both supply and demand sides. The substitution effect is not in line with the expectations of the transition policy. (3) Economic and investment effect are major contributing factors to increase energy demand, while the technical effect plays a significant part in reducing energy demand. A few insights from this study are provided for the formulation of transition policies in China.