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In the context of promoting high-quality development in the Yellow River Basin (YRB) of China, urgent action is needed to achieve the “Dual Carbon” goal through energy savings, emission reductions, and industrial upgrading. This study measures carbon emissions from eight types of energy consumption across 43 industries from 2000 to 2019. Using the Kaya-LMDI model, factors affecting carbon emissions are analyzed, and the relationship between industrial structure and carbon emissions is explored through the coefficient of variation (CV). The findings reveal that coal consumption remains significantly higher than other energy sources, and the effect of energy structure adjustment on carbon emission reduction is limited compared to the impact of energy consumption increase on carbon emission growth. Moreover, the economic output effect is identified as the primary driving factor of carbon emissions, while energy utilization rate is crucial in achieving energy savings and emission reductions. Finally, the CV of carbon emissions across 43 industries is increasing. Based on these results, we suggest several policy recommendations, including prioritizing ecological concerns, developing comprehensive and scientifically sound plans, optimizing energy consumption structure, improving energy utilization efficiency, and adjusting industrial structure to promote sustainable development in the YRB.

This study explores the factors driving CO2 emissions related to energy use in Fujian Province from 2000 to 2019, with an emphasis on long-term trends, short-term fluctuations, and spatial disparities. Utilizing annual data on CO2 emissions and various influencing factors from multiple cities within Fujian Province, we examine the factors driving long-term changes in CO2 emissions. To analyze short-term emission trajectories, we employ a temporal decomposition model, while spatial decomposition techniques are used to assess the variability in emission drivers across 9 prefecture-level cities over different years. Our findings reveal an inverted U-shaped relationship between CO2 emissions and urbanization over the 20-year study period. Furthermore, short-term fluctuations indicate a gradual reduction in the impact of urbanization on the increase in CO2 emissions within the industrial, transportation, and household sectors in Fujian Province. Additionally, economic development, measured as per capita gross domestic product, is shown to significantly influence CO2 emissions. Efforts to reduce energy intensity, which refers to the amount of energy consumed per unit of economic output, in both the industrial and household sectors are identified as potential strategies for emission reduction. The variability in CO2 emissions among cities is primarily attributed to differences in energy intensity and population sizes. These insights are critical for formulating policies aimed at promoting low-carbon development, reducing carbon emissions, and enhancing sustainability throughout Fujian Province.

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.

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.

As the top emitter of carbon dioxide worldwide, China faces a considerable challenge in reducing carbon emissions to combat global warming. Carbon emissions from coal consumption is the primary source of carbon dioxide emissions in China. The decomposition of the driving factors and the quantification of regions and industries needs further research. Thus, this paper decomposed five driving factors affecting carbon emissions from coal consumption in China, namely, carbon emission intensity, energy structure, energy intensity, economic output, and population scale, by constructing a Kaya-Logarithmic Mean Divisia Index (Kaya-LMDI) decomposition model with data on coal consumption in China from 1997 to 2019. It was revealed that the economic output and energy intensity effects are major drivers and inhibitors of carbon emissions from coal consumption in China, respectively. The contribution and impact of these driving factors on carbon emissions from coal consumption were analyzed for different regions and industrial sectors. The results showed that carbon emissions from coal consumption increased by 3211.92 million tons from 1997 to 2019. From a regional perspective, Hebei Province has the most significant impact on carbon emissions from coal consumption due to the effect of economic output. Additionally, the industrial sector had the most pronounced influence on carbon emissions from coal consumption due to the economic output effect. Finally, a series of measures to reduce carbon emissions including controlling the total coal consumption, improving the utilization rate of clean energy, and optimizing the energy structure is proposed based on China’s actual development.

Urban development leads to frequent changes in land use, which leads to temporal and spatial variation of carbon emissions. In this study, land use change data, economic and social data, and energy consumption data of Nanchang City from 2000 to 2020 were used to estimate the spatio-temporal evolution of carbon emissions in Nanchang City, and the LMDI model was used to identify the main driving factors affecting the change of carbon emissions in Nanchang City. It is found that from 2000 to 2020, the carbon emission of Nanchang City has a great change, showing a “parabolic” change law, that is, rising first and then decreasing. Comparing the carbon emissions of various districts and counties, the central city is higher than the surrounding districts and counties. Economic development, land use activities and population status have a positive effect on the increase of carbon emissions, and carbon emission intensity per unit of land and land intensity per unit of GDP have a positive effect on the reduction of carbon emissions. The adjustment and optimization of energy consumption structure and the improvement of human-land policies in Nanchang from the perspectives of optimizing land use layout and structure, rationally controlling population size, and advocating low-carbon lifestyle are conducive to the construction of ecological civilization and sustainable development of Nanchang.

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.

Global climate change imposes significant challenges on the ecological environment and human sustainability. Industrial parks, in line with the national climate change mitigation strategy, are key targets for low-carbon revolution within the industrial sector. To predict the carbon emission of industrial parks and formulate the strategic path of emission reduction, this paper amalgamates the benefits of the “top-down” and “bottom-up” prediction methodologies, incorporating the logarithmic mean divisia index (LMDI) decomposition method and long-range energy alternatives planning (LEAP) model, and integrates the Tapio decoupling theory to predict the carbon emissions of an industrial park cluster of an economic development zone in Yancheng from 2020 to 2035 under baseline (BAS) and low-carbon scenarios (LC1, LC2, and LC3). The findings suggest that, in comparison to the BAS scenario, the carbon emissions in the LC1, LC2, and LC3 scenarios decreased by 30.4%, 38.4%, and 46.2%, respectively, with LC3 being the most suitable pathway for the park’s development. Finally, the paper explores carbon emission sources, and analyzes emission reduction potential and optimization measures of the energy structure, thus providing a reference for the formulation of emission reduction strategies for industrial parks.

Based on the emission factor method and LMDI-LEAP model, this paper systematically studies the current situation, influencing factors and changing trend of carbon emissions from rural buildings in a typical village located in southern China. The results showed that (1) the per capita carbon emissions generated by the energy consumption of rural buildings is 2.58 tCO2/a. Carbon emissions from electricity consumption in buildings account for about 96.07%; (2) the per capita building area, building area energy intensity, population size, population structure and carbon emission coefficient affect rural building carbon emissions, with contribution rates of 70.13%, 31.27%, 0.61%, −1.21% and −0.80%, respectively; (3) from 2021 to 2060, the carbon emissions of rural buildings are expected to increase first and then decrease. In 2021, the base year, carbon emissions from buildings were 2755.49 tCO2. The carbon emissions will peak at 5275.5 tCO2. Measures such as controlling the scale of buildings and improving the utilization rate of clean energy can effectively reduce carbon emissions, in which case the peak can be reduced to 4830.06 tCO2. Finally, the countermeasures and suggestions about rural building energy saving and emission reduction are proposed, including improving the construction management, raising energy efficiency standards in buildings, increasing the proportion of clean energy and raising residents’ awareness of energy conservation.

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.