[Coupling Mechanisms of Eco-environmental Quality and Human Activities in China and Their Influencing Factors].

In the context of sustainable development, it is important to thoroughly investigate the coupling mechanism between China's eco-environmental quality and human activities, as well as identify the influencing factors, in order to provide scientific references for achieving sustainable development goals in China. This study applied trend analysis, coupling coordination degree, LMDI, and optimal parameter geographic detector models to explore and evaluate the coupling mechanism between China's eco-environmental quality and human activities. The findings of the study were as follows:① During the research period, there was a growth trend in China's coupling coordination degree, human activities, and eco-environmental quality. Human activities and coupling coordination degree exhibited a spatial differentiation pattern with the Hu Line as the boundary, showing an "east high, west low" distribution. The eco-environmental quality demonstrated a "south high, north low" differentiation pattern. ② The overall trend of China's coupling coordination type transformation was shifting from lower-level to higher-level coordination types. ③ Based on the geographic detector and LMDI models, the dominant factors influencing the coupling coordination degree in most provinces east of the Hu Line were social and economic factors, as well as the comprehensive coordination index. In contrast, the dominant factors in most provinces west of the Hu Line were natural environmental factors and coupling degree. ④ The evaluation of the impact of changes in human activities on eco-environmental quality revealed that the regions east of the Hu Line were mainly characterized by favorable development and effective protection, whereas the regions west of the line were mainly characterized by destructive development and ineffective protection. It is suggested that the regions on both sides of the Hu Line should prioritize development based on local prerequisites influencing the coupling coordination degree and the relative relationship between human activities and eco-environmental quality. It is crucial to actively adjust development strategies and pursue a sustainable development path towards the high-level coordination between eco-environmental quality and human activities.

Spatio-temporal evolution characteristics of carbon emissions from road transportation in the mainland of China from 2006 to 2021.

The leaping forward of the economy has promoted the rapid growth of road traffic demand, resulting in the carbon emissions of road traffic increasing significantly. It is well known that a one-size-fits-all emission reduction policy is not feasible. Therefore, conducting an investigation on the carbon emissions of all provincial-level regions within a country can assist the government in formulating carbon emission policies at a macro level tailored to different regions. In this study, the whole provincial-level administrative regions in the mainland of China were selected to quantify the carbon emissions of road traffic, and the carbon emissions from 2006 to 2021 were obtained by employing the top-down model. What's more, spatiotemporal characteristics of road transportation carbon emissions in those regions were explored based on Moran's I spatial autocorrelation method. In addition, the LMDI model was constructed based on five driving factors, namely energy intensity, energy consumption intensity, industrial scale, economic development, and population size, and the decomposition analysis of driving factors is carried out. The results show that carbon emissions from road traffic in all provincial regions showed an overall rising trend in the research period, with an average annual growth rate of 11.83 %. The distribution of road transportation carbon emissions exhibited an east-high, west-low distribution, with significantly higher emissions in the eastern and coastal regions compared to inland areas, additionally, China's seven geographical regions showed an initial rapid increase in carbon emissions followed by a stable growth trend. Secondly, five types of spatial clustering were identified of carbon emissions within provincial regions. Thirdly, the impacts of energy intensity and industrial scale were detrimental to road transportation carbon emissions, whereas economic development, energy consumption intensity, and population size had contrasting effects. Implications according to the above conclusions were put forward, aiming to provide guidance for the sustainable development of road transportation and expediting the achievement of the "carbon peaking and carbon neutrality" objective.

The dynamic role of technological innovations and energy structure in China's industrial coal consumption growth: a joint production theoretical decomposition analysis.

China's energy-intensive industries utilize the leading proportion of coal to meet the demand for its industrial outputs, while on the other hand, these industries also assure the provision of livelihood to millions of people, and capping the share of coal consumption for these industries can adversely affect the industrial and economic growth of China. Thus, to achieve the Pareto improvement between environmental pollution and industrial output growth, it is essential to comprehend the patterns of coal consumption in these industries. Hence, the present research intended to analyze the potential drivers of coal consumption by applying a joint LMDI, DEA, and the production theoretical decomposition approach. Findings indices that, first, industrial output growth was the crucial driver to simulate the industrial coal consumption, while the potential coal intensity and coal technology changes exhibited the reverse effect. Second, the coal inputs and industrial output efficiency, along with the improvements in technological gaps, were found to be the imperative factors in decelerating coal consumption. Third, the energy industrial group was discovered to have more potentials of coal conversation as compared to the non-energy industrial group. Moreover, results also indicated that coal pure technical efficiency is accelerating coal growth, which revealed that coal can be saved by enhancing coal allocative efficiency. These findings laid the empirical ground to design a feasible coal conservation policy for achieving the imperative goals of environmental protection without compromising industrial output growth.

Study on the spatial correlation effects and influencing factors of carbon emissions from the electricity industry: a fresh evidence from China.

Carbon emissions from the electricity industry (CEEI) account for a large proportion of China's total carbon emissions, and it is important to study the spatial correlation between CEEI and the influencing factors to promote cross-regional synergistic emission reduction and low-carbon development of the power system. In this paper, the quasi-input-output (QIO) model is applied to assess the transfer of carbon emissions generated by electricity trading based on the consideration of electricity carbon transfer, and the exploratory spatial data analysis (ESDA) method is applied to analyze the spatial correlation effect of carbon emissions from China's electric power sector from 2001 to 2020, analyzes its distribution pattern in both spatial and temporal dimensions, and applies the improved logarithmic mean Divisia index (LMDI) two-stage decomposition model to decompose the changes in CEEI into 11 influencing factors from the perspective of the whole industrial chain of power production, transmission, trade, and consumption. The research results show that (1) the spatial distribution of CEEI has obvious unevenness and aggregation characteristics, with high-high aggregation areas and hot spot aggregation areas generally concentrated in the North China Power Grid and the East China Power Grid, but the aggregation trend is gradually decreasing, while low-low aggregation areas and cold spot aggregation areas are concentrated in the Northwest China Power Grid and the Central China Power Grid, but the area is very limited. (2) The direction of carbon emission diffusion in China's electricity industry is gradually transitioning from southwest-northeast to northwest-southeast, and the east-west diffusion trend is stronger than the north-south diffusion trend and carbon emissions are gradually shifting to the northwest grid. (3) The total amount of electricity production is the most influential factor in the change of CEEI, driving the cumulative growth of CEEI by 4495.34 Mt, followed by GDP per capita and electricity consumption intensity. Coal consumption for power generation, the share of thermal power, and net electricity exports were the main factors inhibiting the increase in carbon emissions from the power sector, with cumulative contributions of -797.74 Mt, -619.99 Mt, and -47.76 Mt, respectively.

[Pathway and Policy for China's Provincial Carbon Emission Peak].

An effective way for China to achieve a carbon emission peak by 2030 is to encourage developed regions to take the lead in attaining carbon peaking at the regional level. Considering Jiangsu Province as an example, this study established a provincial low emissions analysis platform (LEAP-Jiangsu) model. It combined the improved multilevel logarithmic mean Divisia index (M-LMDI) model, Tapio decoupling model, and the synergistic effect of pollution and carbon reduction model to explore the key influencing factors of carbon emissions and carbon reduction paths. The improved M-LMDI model was used to analyze the factors influencing historical and future carbon emissions in Jiangsu Province. Based on the analysis results and planning objectives, a LEAP-Jiangsu model involving various development scenarios was established to predict the time and value of carbon emission peaks. The Tapio decoupling and synergistic effect models were used to clarify the relationship between carbon emissions and economic development, the synergistic effect of carbon, and air pollutant emission reduction. The prediction results demonstrated that the total primary energy demand of Jiangsu Province in 2035 was predicted to be approximately 401.2-474.6 Mt, and the final energy demand would be approximately 319.2-382.3 Mt. Jiangsu Province was most likely to achieve the goal of carbon peaking in 2025-2030, and the peak carbon emission was approximately 815.3-845.7 Mt. The contribution rates of energy conservation and emission reduction measures such as energy intensity reduction, industrial structure optimization, terminal electrification improvement, and energy structure adjustment were 33.1%, 26.8%, 21%, and 15.2%, respectively.

Building carbon peak scenario prediction in China using system dynamics model.

As the issue of global climate change caused by carbon emissions is of great concern, China has proposed achieving its achieve carbon peak goal by 2030. Building carbon emissions account for approximately 50% of China's total carbon emissions. It is crucial to study the time and values of building carbon peaks. In this paper, based on a system dynamics model, logarithmic mean Divisia index model and Monte Carlo simulation, we predict the building carbon peak in China. The following conclusions are obtained: 1) in the baseline scenario, China's building carbon emissions will peak at 5,427 million tons in 2027. In the high-speed development scenario, China's building carbon emissions will peak at 6,298 million tons in 2032. In the coordinated development scenario, the green development scenario, the low-carbon development scenario, and the low-speed development scenario, the peak occurs in 2030 at 5,972 million tons, 5,991 million tons, 5,657 million tons, and 6,329 million tons, respectively. 2) According to the comprehensive simulation, China's building carbon emissions will reach the peak in 2030, with an 80% probability of reaching 5,729-6,171 million tons.

Decomposition and decoupling analysis of multi-sector CO2 emissions based on LMDI and Tapio models: Case study of Henan Province, China.

The peak carbon dioxide emissions at the provincial level is the foundation for achieving the national target of carbon emission peak, thus it is important to analyze the characteristics of provincial CO2 emissions. However, there is a lack of comprehensive analysis and research on quantifying the contributions of the driving factors to decoupling at the provincial level. Therefore, taking Henan Province as the research object, this study establishes the decoupling effort model by combining the traditional LMDI model and Tapio model based on compiling the CO2 emission inventories from 2006 to 2019. The results showed that total CO2 emissions increased from 2006 to 2011, and decreased after 2011 in Henan Province. Raw coal was the primary fuel source of Henan's CO2 emissions, and the sector of "power and heat production" was the major industrial source, accounting for above 45% of the total emissions. Economic output and energy intensity were the major factors promoting and restraining the increase in Henan's CO2 emissions, respectively. In terms of the decoupling state, Henan achieved the transformation from weak decoupling to strong decoupling from 2006 to 2019. Industry presented a strong decoupling condition, while weak decoupling was detected in the agriculture sector during the study period. The changing trend of energy intensity decoupling effort was consistent with that of total decoupling effort, indicating that energy intensity is a crucial factor in achieving decoupling. This study is helpful to grasp the CO2 emission characteristics of Henan Province and provide the theoretical basis for formulating emission mitigation measures of peak carbon dioxide emissions in Henan and other provinces.

Exploring the factors effecting on carbon emissions in each province in China: A comprehensive study based on symbolic regression, LMDI and Tapio models.

Carbon emission (CE) has led to increasingly severe climate problems. The key to reducing CE is to identify the dominant influencing factors and explore their influence degree. The CE data of 30 provinces from 1997 to 2020 in China were calculated by IPCC method. Based on this, the importance order of six factors included GDP, Industrial Structure (IS), Total Population (TP), Population Structure (PS), Energy Intensity (EI) and Energy Structure (ES) affecting the CE of China's provinces were obtained by using symbolic regression, then the LMDI and the Tapio models were established to deeply explore the influence degree of different factors on CE. The results showed that the 30 provinces were divided into five categories according to the primary factor, GDP was the most important factor, followed by ES and EI, then IS, and the least TP and PS. The growth of per capita GDP promoted the increase of CE, while reduced EI inhibited the increase of CE. The increase of ES promoted CE in some provinces but inhibited in others. The increase of TP weakly promoted the increase of CE. These results can provide some references for governments to formulate relevant CE reduction policies under dual carbon goal.

Can China achieve its 2030 and 2060 CO2 commitments? Scenario analysis based on the integration of LEAP model with LMDI decomposition.

China's ambitious targets of peaking its Carbon dioxide (CO2) emissions on or before 2030 and achieving carbon neutrality by 2060 have been a topic of discussion in the international community. This study innovatively combines the logarithmic mean Divisia index (LMDI) decomposition method and the long-range energy alternatives planning (LEAP) model to quantitatively evaluate the CO2 emissions from energy consumption in China from 2000 to 2060. Using the Shared Socioeconomic Pathways (SSPs) framework, the study designs five scenarios to explore the impact of different development pathways on energy consumption and related carbon emissions. The LEAP model scenarios are based on the result of LMDI decomposition, which identifies the key influencing factors on CO2 emissions. The empirical findings of this study demonstrate that the energy intensity effect is the primary factor of the 14.7 % reduction in CO2 emissions observed in China from 2000 to 2020. Conversely, the economic development level effect has been the driving factor behind the increase of 50.4 % in CO2 emissions. Additionally, the urbanization effect has contributed 24.7 % to the overall change in CO2 emissions during the same period. Furthermore, the study investigates potential future trajectories of CO2 emissions in China up to 2060, based on various scenarios. The results suggest that, under the SSP1 scenarios. China's CO2 emissions would peak in 2023 and achieve carbon neutrality by 2060. However, under the SSP4 scenarios, emissions are expected to peak in 2028, and China would need to eliminate approximately 2000 Mt of additional CO2 emissions to reach carbon neutrality. In other scenarios, China is projected to be unable to meet the carbon peak and carbon neutrality goals. The conclusions drawn from this study offer valuable insights for potential policy adjustments to ensure that China could fulfill its commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060.

Spatiotemporal heterogeneity and decoupling decomposition of industrial carbon emissions in the Yangtze River Delta urban agglomeration of China.

The objective of this study is to identify the spatiotemporal change law and the leading factors of industrial carbon emission decoupling. Based on the industrial carbon emission level of the Yangtze River Delta urban agglomeration (YRDUA) from 2006 to 2020, the spatiotemporal heterogeneity was explored with the help of the spatial Markov chain, the Tapio decoupling model was used to analyze its decoupling state from the industrial economy, and its driving factors were decomposed using the Kaya identity and logarithmic mean Divisia index (LMDI) model. The results show that (1) in 51.9% of the YRDUA's cities, the industrial carbon emission situation was stable, the emission reduction observation area (medium carbon) occupied a dominant position, and the emission reduction key area (relatively high carbon) gradually decreased. (2) Industrial carbon emissions had spatial overflow and path dependency characteristics, and the probability of carbon emission type transfer maintaining the original state reached 80.0%. From 2006 to 2011, the average probability of the downward migration of high-carbon cities was 5.0%. From 2011 to 2020, the average probability of the upward transfer of low-carbon cities was 9.4%. (3) The negative decoupling rate of carbon emissions in the YRDUA experienced a transition from 3.7% to 44.4% and then back to 7.4%, showing spatial imbalance. Unsatisfactory decoupling cities were concentrated along the Yangtze River and in coastal areas. (4) The promoting efficiency of energy intensity, carbon emission coefficient, and employment structure was gradually strengthened, and the carbon-increasing effect of labor input was gradually weakened. (5) The decoupling mode of heavy difficult cities is dominated by the three-factor balanced type, which is jointly affected by industrial production, labor input, and carbon emission coefficient. The findings in this study can provide inspiration for industrial carbon emission reduction in megalopolises.

Decomposing the decoupling relationship between energy consumption and economic growth in China's agricultural sector.

Sustainable food supply is affected by high energy consumption and negative environmental effects. Regarding the national strategy of "carbon peaking and carbon neutrality targets", the decoupling between energy consumption and economic growth in China's agriculture has received significant attention. Therefore, this study first presents a descriptive analysis of the energy consumption in China's agricultural sector from 2000 to 2019, before analyzing the decoupling state between energy consumption and agricultural economic growth at the national and provincial levels using the Tapio decoupling index. Finally, the logarithmic mean divisia index method is used to decompose the decoupling driving factors. The study draws the following conclusions: (1) At the national level, the decoupling of agricultural energy consumption from economic growth fluctuates among expansive negative decoupling, expansive coupling, and weak decoupling, before stabilizing in the last state. (2) The decoupling process also differs by geographic region. Strong negative decoupling is found in North and East China, and strong decoupling lasts longer in Southwest and Northwest China. (3) The factors driving decoupling are similar at both the levels. The economic activity effect promotes the decoupling of energy consumption. The industrial structure and energy intensity effects are the two main suppressive factors, whereas the population and energy structure effects have relatively weaker impacts. Therefore, based on the empirical results, this study provides evidence for regional governments to formulate policies on the relationship between the agricultural economy and energy management from the perspective of effect driven policies.

Driving factors of NOX emission reduction in China's power industry: based on LMDI decomposition model.

Under the policy background of "joint prevention and control" of global greenhouse gas emission reduction and regional air pollutants, the power industry, as an important target industry of energy conservation and emission reduction policies, has become an effective choice to release dual pressures. In this paper, the "bottom-up" emission factor method was used to measure the emission of CO2 and NOX from 2011 to 2019. Then, the contributions of six factors to NOX emission reduction in China's power industry were identified using the Kaya identity and logarithmic mean divisia index (LMDI) decomposition methods. The research results show that (1) there is a significant synergistic emission reduction effect between CO2 emission reduction and NOX emission reduction; (2) the factor that inhibits the growth of NOX emissions reduction in the power industry is economic development factor; and (3) the main factors that promote the reduction of NOX emission from the power industry are synergy effect, energy intensity, power generation intensity, and power production structure factors. Several suggestions are proposed, which are the power industry should adjust its structure, improve energy intensity, focus on applying low-nitrogen combustion technology, and improve the air pollutant emission information disclosure system to reduce NOX emissions.

Spatiotemporal Characteristics, Decoupling Effect and Driving Factors of Carbon Emission from Cultivated Land Utilization in Hubei Province.

The carbon emission level and spatiotemporal characteristics in Hubei Province were estimated and studied using the Intergovernmental Panel on Climate Change (IPCC) carbon emission coefficient technique based on county data from Hubei Province from 2000 to 2020. The relationship between carbon emissions from cultivated land utilization and agricultural economic growth was examined using the Tapio decoupling index, and the factors influencing carbon emissions in Hubei Province were further examined using the Logarithmic Mean Divisia Index (LMDI model). The results demonstrate that: (1) Spatiotemporal variations in carbon emissions are evident. In terms of time, the volume of carbon emissions in Hubei Province is still substantial, and the transition to low-carbon land use is quite gradual. Geographically, the high-value region of the middle east coexists with the low-value zone of the west, with apparent regional contrasts. (2) The decoupling between carbon emissions and agricultural economic growth is becoming more and more obvious in Hubei Province. The number of counties and cities in a negative decoupling state has significantly decreased, and the majority of counties are now in a strong decoupling condition. (3) Agricultural production efficiency is the most significant driving factor for restricting carbon emission, according to the decomposition results of carbon emission driving factors based on the LMDI model. In addition, the results of sample decomposition based on topographic characteristics indicate that agricultural production efficiency is primarily responsible for the suppression of carbon emissions in flat regions. The increase in carbon emissions in hilly regions is primarily influenced by agricultural productivity. The increase in carbon emissions in mountainous regions is mostly influenced by agricultural labor intensity. This study's finding has enlightening implications for the high-quality growth of agriculture.

Exploration of Spatio-Temporal Characteristics of Carbon Emissions from Energy Consumption and Their Driving Factors: A Case Analysis of the Yangtze River Delta, China.

For the Yangtze River Delta (YRD) region of China, exploring the spatio-temporal characteristics of carbon emissions from energy consumption (CEECs) and their influencing factors is crucial to achieving carbon peaking and carbon neutrality as soon as possible. In this study, an improved LMDI decomposition model based on the Tapio model and Kaya's equation was proposed. Combined with the improved LMDI and k-means cluster analysis methods, the energy structure, energy intensity, unit industrial output value and population size were selected as the driving factors, and the contribution of each driving factor to the CEECs of prefecture-level cities was quantitatively analyzed. Our study found that: (1) By 2020, the total amount of CEECs in the 26 prefecture-level cities in the YRD will stabilize, while their intensity has shown a downward trend in recent years. (2) The decoupling relationship between CEECs and economic development generally showed a trend from negative decoupling to decoupling. The dominant factor in decoupling was generally the shift of DEL values towards urbanization rate and energy intensity and the open utilization of energy technologies. (3) From 2000 to 2010, the dominant factors affecting CEECs in 26 cities were energy intensity and energy structure, followed by industrial output value and urbanization rate. In general, the promotion effect of economic development on carbon emissions in the YRD region was greater than the inhibitory effect. After 2010, the restrictive effect of various factors on CEECs increased significantly, among which the role of gross industrial output was crucial. The research results can provide a scientific policy basis for the subsequent spatial management and control of carbon emission reduction and carbon neutrality in the YRD region at a finer scale.

Spatiotemporal dynamics and decoupling mechanism of economic growth and carbon emissions in an urban agglomeration of China.

Carbon emissions and economic growth are two contradictions in urban development, and their decoupling is related to the sustainable development of cities. This paper took urban agglomeration in the middle reaches of the Yangtze River (UAMRYR), China, as the study area. The Kaya model, the Tapio decoupling model, and the Logarithmic Mean Divisia Index (LMDI) model were adopted to analyze the spatiotemporal differentiation of carbon emissions, the decoupling of economic activities, and driving factors. The results indicate that (1) carbon emissions increased by 66% in the study period, but the growth momentum was curbed after 2015. Low level and medium level areas continue to decrease, and relatively high level area gradually become dominant. (2) Spatially, carbon emissions are in a pattern of middle-hot and east-cold. Jiangxi is in the sub-cold and coldspot area, while the hotspot area is driven by the transformation from Wuhan's single-core to Wuhan and Changsha's dual-core. (3) Since 2010, most cities have been in a good decoupling state, and weak decoupling cities have risen from 35.5% in the initial period to 87.1% in 2010-2011, but the decoupling situation of industrial cities with more high-energy-consuming industries still rebounded slightly. (4) The economic level and energy intensity effect had the most significant impact on the economic decoupling of carbon emissions, whose absolute contribution rates were greater than 35%. Urbanization and economic level both play a positive role in promoting carbon emissions, and the energy intensity plays a negative role in retarding carbon emissions. The population effect was mainly manifested in carbon increase from 2006 to 2011, and 45.2% of the cities from 2011 to 2017 turned into carbon suppression. Finally, we suggest that decoupling carbon emissions from economic growth requires developing green urbanization and a decarbonized economy, optimizing the structure of energy consumption and guiding rational population flow.

Decoupling between Economic Development and Carbon Emissions and Its Driving Factors: Evidence from China.

Analyzing the relationship between economic development and carbon emissions is conducive to better energy saving and emission reduction. This study is based on the panel data of China's carbon emissions, from 2009 to 2019, and quantitative analysis of the relationship between carbon emissions and economic development through the Tapio decoupling model and the Logarithmic Mean Divisia Index (LMDI) decomposition model. The results show that: First, carbon emission and economic development are increasing year by year, and the development trend of economic growth rate and carbon emission growth rate presents the characteristics of consistency and stage. Second, China's carbon emissions and economic development are basically in a weak decoupling state, and carbon emissions and economic development are positively correlated. Third, there are significant differences in decoupling indices among the four regions, mainly in that the central region is better than the eastern region, the eastern region is better than the northeast region, the northeast region is better than the western region, and the development of provinces in the region is unbalanced. Fourth, from the perspective of driving factors, the elasticity of population size and economic intensity can restrain the decoupling of carbon emissions, while the elasticity of energy intensity and carbon intensity have a positive effect. Finally, according to the results of empirical analysis, this paper focuses on promoting China's emission reduction and energy sustainable development from the aspects of developing low-carbon and zero carbon technology, supporting new energy industries and promoting the construction of a carbon emission trading market.

Characteristics, decoupling effect, and driving factors of regional tourism's carbon emissions in China.

By revealing the temporal and spatial differentiation of China's regional tourism carbon emissions and its decoupling relationship with tourism economic growth and identifying the key factors affecting tourism carbon emissions, this paper is expected to provide a reference for the formulation and implementation of China's regional tourism industry emission reduction policies and measures. Using the tourism's carbon emission data of 30 provinces (cities) in China from 2007 to 2019, we have established a logarithmic mean Divisia index (LMDI) model to identify the main driving factors of carbon emissions related to tourism and a Tapio decoupling model to analyze the decoupling relationship between tourism's carbon emissions and tourism-driven economic growth. Our analysis suggests that China's regional tourism's carbon emissions are growing significantly with marked differences across its regions. Although there are observed fluctuations in the decoupling relationship between regional tourism's carbon emissions and tourism-driven economic growth in China, the data exhibit a primary characteristic of weak decoupling. Nonetheless, the degree of decoupling is rising to various extents across regions. Three of the five driving factors investigated are also found to affect emissions. Both tourism scale and tourism consumption lead to the growth of tourism's carbon emissions, while energy intensity has a significant effect on reducing emissions. These effects differ across regions.

Financing for energy efficiency solutions to mitigate opportunity cost of coal consumption: An empirical analysis of Chinese industries.

This study measures the energy rebound effects of Chinese energy and coal power use in Chinese energy-intensive industries by using latent class stochastic frontier models like LMDI, and other various econometric estimation approach for coal-supplying regions in China ranging between 1992 and 2018. The findings reveals that China's coal sector's average capacity consumption is 0.81%, with a pattern of first increasing and then decreasing, falling to 0.68% in 2016 specifically. The coal capacity operation rate concerning low as well as depleted regions is generally strong, with limited space for expansion. In 2015 and 2016, the utilization rate of coal production potential in moderate-producing areas fell about 42%. Economic development variables affect the capacity utilization levels of moderate, weak, and depleted generating regions. At the same time, the price volatility cannot induce a practical improvement in the ability utilization rate, which means that China's coal industry is mainly un-marketized. China's energy efficiency increased about 19.98% among 2000 and 2016, while the rapidest expansion pattern has been noted in the eastern province at 39.86%, next to central (11.71%) and western regions (9.59%). The take back impact via the renewable energy and renewable productivity channels is estimated as 12.34% and 25.40%, respectively. Therefore, the take back impact is of significant importance regarding energy preservation, as China's cumulative renewable energy use is equal to China's aggregate energy use. On such findings, recent research also contributed by presenting novel policy implications for key stakeholders.

Decomposition analysis of PM2.5 emissions based on LMDI and Tapio decoupling model: study of Hunan and Guangdong.

This paper takes energy consumption PM2.5 emission as research object, and quantitatively analyzes the PM2.5 emission level in Hunan and Guangdong provinces from 2012 to 2017. We build a PM2.5 emission decomposition model divided by five sectors, including industry, transportation, construction, resident, and other, and use attribution method and Tapio decoupling index to analyze the relationship between economic development and PM2.5 emission level. The results show that (1) the difference in PM2.5 emissions between the two provinces appeared in 2015; (2) the contribution rate of total PM2.5 emissions is 83.1%, and coal consumption is the determine factor of PM2.5 emissions; industry is the main source of sector contribution with rate of 70.91%; (3) Guangdong's pollution control capacity is much higher than that of Hunan, while Hunan's PM2.5 marginal emission-reduction potential is much higher than that of Guangdong; (4) economic growth is the first increasing emission reason of PM2.5 emission changes, while the intensity of industrial energy consumption is the first reduction emission reason; (5) there is a big difference between the economic development of the two provinces and the decoupling of PM2.5 pollution.

Decomposition study of energy-related CO2 emissions from Bangladesh's transport sector development.

The transportation sector has a dominant contribution to the fast-growing economy of the developing country Bangladesh. However, the nature of operating the transportation sector in the country requires an excessive amount of fossil energy which causes the rise of CO2 emissions. Ascertaining the impending factors and technologically to conserve energy, as well as governing CO2 emissions from this sector, are essential to attain sustainable development. The paper endeavors to determine the decomposition of driving factors that affect the relationship between Bangladesh's transport sector development and CO2 emissions due to energy consumption from the year 1990 to 2017 using the Logarithmic-Mean Divisia Index (LMDI) model. The decomposition factors are fragmented into five elements through consideration of five fossil energies which are used in Bangladesh's transportation sector. The result reveals a 106.94% growth of aggregate CO2 emissions in the transportation sector of Bangladesh. The results also show that aggregate influence of economic activity factor, population factor, economic structure factor, and energy intensity factor liable in increase CO2 emissions to 66.03%, 23.56%, 7.64%, and 6.25% respectively. On the contrary, the energy structure factor is accountable for the decrease in CO2 emissions to - 0.80%. Thus, the Bangladesh Government should proliferate mass responsiveness programs and cope with economic development through emphasizing quality of development rather than quantity which will ensure sustainable transport sector development.