Industry carbon emissions have been increasing, yet there remains a dearth of research on the impacts of climate policy uncertainty. This study first explored the effects of climate policy uncertainty on the carbon emissions of semiconductor enterprises. We employed the Bidirectional Encoder Representations from Transformers (BERT) model. We constructed a Chinese climate policy uncertainty index based on electronic news entries to match the enterprise panel data structure from 2011 to 2022. The results showed an increase in climate policy uncertainty, which helped to reduce semiconductor enterprises’ carbon emissions. This effect was primarily achieved via two pathways. First, climate policy uncertainty leads to companies facing stricter environmental requirements, and these companies will proactively increase their investment in environmental, social, and governance standards to cope with the potential risks. Second, climate policy uncertainty is often accompanied by shifts in government climate policy. Governments will provide green subsidies to enterprises to achieve their policy goals. Furthermore, the policy uncertainty for the semiconductor industry could amplify the reducing effect of climate policy uncertainty on the carbon emissions from semiconductor enterprises. Climate policy uncertainty has a greater impact on non-state-owned and smaller semiconductor enterprises. Our study provides a new way to measure climate policy uncertainty, finds a new perspective based on climate policy uncertainty for exploring the potential impacts of corporate carbon emission reductions, bridges the gap between previous studies on enterprise carbon reductions and climate policy uncertainty, and offers a new path for governments to manage industrial carbon emissions.

Cross-border cooperation can overcome the administrative boundaries of water pollution governance, and the two-way ecological compensation model emerges as a solution to transforming government-financed payments into locally-driven development initiatives. This study presents a tripartite evolutionary game model for two-way ecological compensation, incorporating city-level data, and employs time-varying difference-in-differences to estimate the incentive impact of China's cross-border horizontal ecological compensation policy on water pollution governance during 2006–2018. Furthermore, this study aligns state-controlled monitoring stations with their respective administrative districts and enhances water pollution indicators obtained from these stations for a more comprehensive policy assessment. The results show that horizontal ecological compensation effectively reduces the prevalence of industrial wastewater discharges and associated water pollution; this effect is more pronounced upstream (20.94%) compared to downstream (16.01%). However, it does not significantly impact urban sewage treatment, agricultural non-point source pollution, or lake water quality and eutropHication. In addition to promoting horizontal coordination between upstream and downstream regions, vertical central governmental inspections are crucial for effectively stimulating and guaranteeing horizontal collaborative governance between upstream and downstream local governments. These results provide strong empirical evidence in favor of expanding horizontal ecological compensation initiatives throughout the entire river basins, as well as in other countries or regions.

Energy poverty eradication is essential for achieving sustainable development goals; however, household financial participation behavior in eliminating energy poverty has not been received much attention. In this study, the impact of financial market participation on household energy poverty was investigated by applying the multidimensional energy poverty index measured by the entropy method and using data from the 2015 Chinese General Social Survey. The results showed that financial market participation significantly reduced household energy poverty. Meanwhile, future expectation was an important mediating mechanism in this process. Moreover, the increase in financial risk reduced the effect of future expectation on household energy poverty because higher financial risk weakened household financial stability. The findings offered a new perspective on eradicating household energy poverty by promoting financial market participation.

The transition of households towards cleaner energy is crucial for achieving sustainable development goals. However, the impacts and associated mechanisms of early-life experiences on household energy transition have not been considered. Based on data from the 2015 Chinese General Social Survey, this study aimed to investigate whether people experiencing China's Great Famine (1959-1961) in their early life promoted household energy transition in adulthood. The varying severity of the Great Famine in different provinces was characterised as a quasi-natural experiment and was used to perform difference-in-differences (DID) estimation analysis for birth cohorts. The results showed that the transitions from firewood, agricultural waste, and animal waste to liquefied petroleum gas and electricity were significant in households with the Great Famine experiences. Specifically, the long-term energy transition effect of the famine was exhibited mostly in those who experienced the famine during childhood (4-11 years old) and adolescence (12-17 years old). Besides, early-life famine experiences led to poor physical health, and more modern forms of energy, such as electricity, were consumed to avoid further deteriorating health. Early-life famine experience also brought psychological trauma to people at that time, which led them to increase Internet use to gain emotional support, and the increased Internet use provided better access to information about the energy transition. Moreover, the household energy transition influenced by early-life famine experience occurred more in female-headed, rural, more educated, and low-income households. Our results illustrated the role of early-life famine experience in household energy transition and provided new insights into developing effective energy policies.

Global warming is one of the largest challenges humankind is facing in this century, and how to achieve low-carbon economy has become one of the most attractive topics of global concern. However, evaluations of the low-carbon economy are insufficient due to limited methodologies and data availability. In this study, satellite data (i.e., night-time light data and net primary production) were employed to estimate the net economic output (neo), and ratio of neo to the GDP (reo), which can be used to assess the quantity and quality of worldwide low-carbon economies. Based on panel vector autoregression (pvar) analysis, we further discussed the drivers of neo and reo in global climate change mitigation towards a better low-carbon society. The results show that: (1) only France and the United Kingdom ranked within the top 10 in terms of the neo and reo in 2019, implying that they were successful in increasing both quantity and quality of low-carbon economic development; (2) the pvar analysis presented that the increase of reo granger-caused neo growth, and net primary production increment greatly helped raise the worldwide reo; (3) raising CO 2 abatement policy stringency can play a major role in improving the quality of low carbon economy countries with poor quantity and quality, but it cannot significantly promote groups with high reo. Additionally, the results of this study also provided basic data, such as our calibrated global 1 × 1 km gridded night-time light data during 1992–2019 for research regarding low-carbon economy and other sustainable development issues.

Understanding the evolution of energy consumption and efficiency in China would contribute to assessing the effectiveness of the government's energy policies and the feasibility of meeting its international commitments. However, sub-national energy consumption and efficiency data have not been published for China, hindering the identification of drivers of differences in energy consumption and efficiency, and implementation of differentiated energy policies between cities and counties. This study estimated the energy consumption of 336 cities and 2,735 counties in China by combining Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS) and Suomi National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) satellite nighttime light data using particle swarm optimization-back propagation (PSO-BP). The energy efficiency of these cities and counties was measured using energy consumption per unit GDP and data envelopment analysis (DEA). These data can facilitate further research on energy consumption and efficiency issues at the city and county levels in China. The developed estimation methods can also be used in other developing countries and regions where official energy statistics are limited.

The data of city-and-county-level energy consumption and energy efficiency provided in this study are valuable with practical applications in the field of energy economics, management, and policy, including the following: First, the provided data have the characteristics of wide spatial coverage and long time span. This unique panel-structured dataset can be used to observe the trajectories and spatial differences of energy consumption and energy efficiency on a micro-level than at national and provincial levels. Therefore, it can also be used to analyze the factors driving the changes and spatial differences of energy consumption and energy efficiency in cities and counties. Second, the panel-structured dataset of energy consumption and energy efficiency can be used to match other economic data at the city and county levels, and studies such as the economic effects of energy consumption, the environmental and social effects of energy consumption, and the coupling relationship between energy efficiency and economic development may be conducted. Third, the development of energy consumption and energy efficiency data at the city and county levels can not only contribute to the energy management at China’s grassroot-level governments, such as in the formulation and implementation of road maps for energy transformation and energy efficiency improvement, but also provide a basis for the central and provincial governments to allocate the energy rights of cities and counties under the constraints of “carbon peak” and “carbon-neutral” targets. Fourth, the development of energy consumption and energy efficiency data at city and county levels could provide a more accurate assessment of the impact of the central government’s energy saving, emission reduction, and low-carbon green policies, as well as other socioeconomic policies, for example, assessment of the impact of the “central heating,” “coal to electricity,” low-carbon pilot city, and carbon emission trading right pilot policies on energy consumption and energy efficiency. Fifth, the method of retrieving micro-level energy consumption data by using satellite night-light data can also provide a reference for other developing countries and regions with limited energy statistics to evaluate their energy consumption and energy efficiency at the sub-national level.

Differentiated policies are key to improving the CO2 emissions reduction efficiency of cities, which are vital in mitigating climate change. The K-means cluster method and spatial logarithmic Divisia index decomposition method were used on the data of 279 cities in China to examine the impacts of local public expenditure on CO2 emissions in the context of socio-economic conditions. The results show emission differences in cities with similar socio-economic conditions. The impacts of the carbon intensity of local public expenditure and other public expenditures on the emission differences of city groups, which have different socio-economic conditions, were largest, followed by the local public expenditure scale and public environmental expenditure. The impacts of the proportions of public environmental expenditure and other expenditures were limited. Insights gained can provide feasible implications for Chinese cities, and enable policymakers to focus on the impact of different fiscal policies on CO2 emissions differences between cities.

Spatial differences in CO2 emissions must be taken into account in CO2 mitigation. In this work, a spatial within-between logarithmic mean Divisia index decomposition model was developed by using cluster analysis to evaluate the potential role of fiscal decentralization in driving interprovincial differences in CO2 emissions in China. The results revealed that the direct impact of fiscal decentralization emerged as a major emission driver after 2009. The differences of provincial CO2 emissions from the national average can be mainly attributed to emission differences between the distinct provincial clusters. The direct and indirect impacts of fiscal decentralization contributed to the shaping of differences in CO2 emission between provinces and their provincial cluster average, and between provincial cluster average and the national average. Reducing the differences in CO2 emission between distinct provincial clusters should be considered a breakthrough for the Chinese government. The provinces with CO2 emissions below the national average and above the average emissions of its provincial cluster still have the potential for further mitigation. Optimizing the expenditure authority of the central and provincial governments and improving the energy efficiency of the provincial fiscal expenditure are the two effective ways to further promote CO2 mitigation.

Accurate, long-term, full-coverage carbon dioxide (CO2) data in units of prefecture-level cities are necessary for evaluations of CO2 emission reductions in China, which has become one of the world's largest carbon-emitting countries. This study develops a novel method to match satellite-based Defense Meteorological Satellite Program's Operational Landscan System (DMSP/OLS) and Suomi National Polar-orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light data, and estimates the CO2 emissions of 334 prefecture-level cities in China from 1992 to 2017. Results indicated that the eastern and coastal regions had higher carbon emissions, but their carbon intensity decreased more rapidly than other regions. Compared to previous studies, we provide the most extensive and long-term CO2 dataset to date, and these data will be of great value for further socioeconomic research. Specifically, this dataset provides a foundational data source for China's future CO2 research and emission reduction strategies. Additionally, the methodology can be applied to other regions around the world.

This paper evaluates the impact of fiscal decentralization on CO2 emissions in China. We build an equation to combine fiscal decentralization and CO2 emissions and decompose it using the logarithmic mean Divisia index (LMDI) decomposition technique. Then, we examine the nonlinear impact of fiscal decentralization on CO2 emissions using a dynamic panel regression model and Chinese interprovincial data from 1997 to 2015. The empirical results show that the direct impact of fiscal decentralization on CO2 emissions is nonlinear, and the higher the per capita fiscal expenditure, the more fiscal decentralization can reduce CO2 emissions. Moreover, the government’s emissions reduction policies effectively reduce CO2 emissions only when they are conducted at the provincial and city levels, and some differences among regions remain.

This study constructs an index of humanitarian labor efficiency to measure labor utilization based on a factor-specific data envelopment analysis model. Compared with the traditional labor productivity index, humanitarian labor efficiency is a more reliable and comprehensive measure; it not only excludes the contribution of non-labor input but also considers the undesirable output, such as accidental deaths. The results show that China's humanitarian labor efficiency is low, ranging from 0.3 to 0.7, and it should be improved further. Unlike labor productivity, humanitarian labor efficiency did not increase markedly between 2007 and 2017. Further analysis revealed that pure labor efficiency declined while pure humanitarian efficiency increased for this period, which offset each other. Tobit regression shows that industrial structure has a significant influence on humanitarian labor efficiency. Baumol's disease, caused by the growing tertiary industry, may result in the decline of labor utilization efficiency in China. This study presents suggestions on how to deal with Baumol's disease.

Assessing emissions-reduction pressure among Chinese cities is a critical task for local governments formulating and implementing environmental policies. From the perspectives of carbon intensity and carbon inequality, this study develops an improved carbon dioxide (CO2) emissions-reduction index to quantify emissions-reduction pressure on 284 cities in China. Results indicate that driven by the decrease of overall carbon intensity and the rise of inter-city carbon inequality, emissions-reduction pressure on 41.38% of provinces and 49.65% of cities was greater than the overall national level; emissions-reduction pressure on 52.35% of cities exceeded the provincial average level. The central government determines national emissions-reduction pressure by adjusting carbon-inequality tolerance between cities and determines carbon-inequality preference based on population and economic output principles. These determinations become benchmarks for local governments' CO2 emissions-reduction pressure. Provinces and cities that exceed benchmarks become foci for promoting energy savings, emissions reduction, and low-carbon development in the future.

The expansion of fossil fuel consumption, particularly that of coal, drives global socio-economic development and causes large-scale emissions of carbon dioxide (CO2). The key to the reduction of global CO2 emissions lies in whether major CO2 emitters can meet established reduction goals. This paper reviews the histories of China, the United States, and India in terms of their respective CO2 emission pathways, reflects on the motivations and mechanisms behind these changes, and predicts whether these three major CO2 emitters can control their coal consumption and promote reduction of global CO2 emissions. We conclude that China is decreasing its coal consumption, India is experiencing irreversible, increase of coal consumption, and the use of coal in the U.S. will depend largely on the energy and climate policies of the Trump administration. To achieve the goal of reduction of global CO2 emissions, the international community should not only increase its scrutiny of coal consumption and CO2 emissions in India but also emphasise the role of international cooperation, which is the only solution to tackling the reduction of CO2 emissions and limiting anthropogenic climate change as economic systems continue to globalise.

This study analyses the changes in energy-related carbon dioxide (CO2) emissions of the agricultural sector in China from 2005 to 2013. Using the logarithmic mean Divisia index (LMDI) decomposition method, this study attributes the changes in agricultural CO2 emissions to agricultural CO2 emissions intensity, agricultural productive income intensity, rural residents' income structure, the distribution pattern of residential income, the distribution pattern of national income, economic development, provincial population distribution, and population scale, and treats these factors as technology, distribution, and population effects. Based on this, the nested decomposition problem, which has not been mentioned in related studies, is solved. To emphasize the importance of the logarithmic mean weight functions, two different chain LMDI decomposition methods are developed that are based on differences in the logarithmic mean weight functions. The results show that the distribution pattern of national income and rural residents' income structure are two key factors that separately stimulate and suppress the changes in China's agricultural energy-related CO2 emissions. After nested decomposition of the distribution pattern of residential income, the suppressing influence from the rural population proportion is stronger than the stimulating influence from rural-urban income inequity. Although the results of the two chain LMDI decomposition methods are similar, only the distribution pattern of national income and rural residents' income structure maintain positive impacts on the changes in China's agricultural CO2 emissions by year, while the rural residents' income structure, distribution pattern of residential income, and rural population proportion continue to have negative impacts on changes in China's agricultural CO2 emissions by year. Furthermore, the technology, distribution, and population effects could not suppress China's agricultural CO2 emissions simultaneously in most years.

采用分布函数拟合居民收入分布需要解决两个关键问题,即找到恰当的分布函数与合适的估计方法.文章基于国家统计局提供的2011年城镇居民收入分组数据以及安徽省统计局提供的微观城镇住户调查数据,运用常见的六种分布函数依次拟合了中国城镇居民的收入分布.随后,根据不同的检验标准,比较了新估计方法、极大似然估计以及广义矩估计的拟合效果.研究发现,广义第2类beta分布函数对中国居民收入分布的拟合效果最好,同时,极大似然估计方法的参数估计误差相对较小.不过,在选择具体方法时,还应考虑实际的数据结构以及对计算结果的偏好.

This paper develops the traditional form of LMDI decomposition method by introducing financial energy conservation and financial development.Integrating the decoupling elastic index and using the provincial data of China from 1997 to 2015,the paper examines the effects of CO2 emissions which influenced by these two factors.The results show that:Firstly,although the scale of CO2 emissions in China expanded rapidly from 1997 to 2015,the short-term evolution trend can be divided into three periods,namely,expansion,declining and stable periods.Secondly,financial energy conservation,financial development and economic development are three major factors which cause the changes of CO2 emissions in short and long terms;besides,the impacts of the former two factors on CO2 emissions are almost larger than the later one.Thirdly,the influence directions of financial energy conservation and financial development are different.Finally,the decoupling elastic states between financial energy conservation and CO2 emissions,financial development and CO2 emissions keep week decoupling or strong negative decoupling in short term,and strong negative decoupling in long term.After decomposing the causes of those decoupling elastic states,not only the influence directions of direct and interactive factors are always opposite,but also the influence directions of each factor in the two decoupling elastic values are contrary.

Measuring regional differences in fossil energy consumption is the first step to the study of natural resource allocation and utilization. This paper employs annual and cumulative consumption Gini indexes as well as the deviation index to discuss regional differences in the per capita consumption of fossil energy and related products across 30 Chinese provinces from 1997 to 2013. The results show that Chinese inter-provincial Gini ratio of fossil energy consumption is below 0.3 in recent years, and change in per capita energy consumption is the key factor behind the decline in the overall Gini index. Unlike existing studies based only on annual flows of energy consumption, this paper also focuses on cumulative energy consumption. Moreover, decomposing annual and cumulative consumption Gini coefficients by type, group, and incremental variation is rarely seen in other studies. Based on the above positive analysis, the paper provides some policymaking suggestions.

Coal is one of the main fuel sources in China. This paper sheds light on the evolution of China's interregional differences in CO2 emissions from coal by constructing a Gini coefficient and decoupling elasticity index for emissions from 1997 to 2012 and explains why emission differences deviate from economic growth differences. The study decomposed the Gini coefficient of CO2 emissions from coal by source, incremental source, and region. It also divided the decoupling elasticity of carbon emissions into two components: effects of environmental expenditure and effects of emission reduction policy. The findings of the study are as follows: First, interregional differences in China's overall CO2 emissions from coal are characterized by periodic fluctuation. Second, the differences in emissions from raw coal, the concentration effect of emissions, and the emission differences within regions are the three main factors in the overall difference changes in coal's carbon emissions in China. Last but not least, the decoupling between provincial CO2 emissions from coal and economic growth is on the whole weak. Based on the above findings, the author offers four suggestions for emission reduction.