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A major goal of the "14th Five-Year Plan" phase is to promote the green transformation of industrial enterprises to address the 'dual carbon' challenge. Utilizing the China Industrial Enterprises Database and the Polluting Enterprises Database, this paper calculates the carbon emissions of Chinese industrial enterprises from 2001 to 2010 at the micro level. It presents an analysis of the heterogeneity of carbon emission efficiency (TPI) in industrial enterprises, as well as the factors influencing corporate TPI. This study finds that enterprises within a subdivided industry exhibit heterogeneous levels of TPI, with carbon emissions largely affected by the structure of energy consumption. The researchers suggest accelerating the transition of industrial enterprises to green technology and argue that carbon emission policies should shift from controlling direct total targets to strengthening market-oriented policy tools. Carbon reduction targets should be more stringent for enterprises with lower TPI, considering the heterogeneity among enterprises. To meet the challenges of emission reduction, industrial enterprises are encouraged to actively reform their energy consumption structure. Government policies should aim to reduce clean energy costs and encourage the use of clean energy by industrial enterprises. [ABSTRACT FROM AUTHOR]

This document is a list of academic articles and research papers related to carbon emissions and their effects. The articles cover various topics such as the analysis of carbon emissions in different regions, the driving factors of carbon emissions, and the impact of carbon emissions on the environment. The articles provide valuable insights into the complex issue of carbon emissions and can be useful for library patrons conducting research on this topic. [Extracted from the article]

Urban carbon emissions are one of the most important areas contributing to the growth of carbon emissions, and resource-dependent cities with natural resource extraction and processing as their leading industries tend to have higher carbon emissions. Ordos is the city with the highest coal production in China, and its economic development is dominated by coal, oil and gas, and other resource extraction and processing industries, with industrial activities making a large contribution to carbon emissions. At the same time, Ordos has undergone rapid industrialization in recent years, but still faces the problem of environmental pollution, epitomizing a typical resource-dependent city in China. Therefore, this paper takes Ordos as an example and uses the Generalized Divisa Index Method (GDIM) to study the drivers of industrial carbon emissions in Ordos from 2005–2020, a typical resource-dependent city in China, and further analyzes are conducted in relation to the three phases of development. Based on the key drivers, the Monte Carlo method is used to forecast industrial carbon emissions from 2021 to 2030. The results show that the most important factors driving the growth of industrial carbon emissions are the scale of industrial output and industrial energy consumption, while the intensity of industrial energy investment is the most important factor mitigating industrial carbon emissions, and that energy efficiency and carbon intensity of energy consumption can also mitigate carbon emissions after economic transformation. At the same time, investment is the factor with the greatest potential for optimization on the path to emissions reduction. [ABSTRACT FROM AUTHOR]

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The excessive carbon emissions not only intensify the global climate change, but also seriously restrict the sustainable development of social economy. However, improving industrial carbon emissions efficiency is the most directly effective way to reduce emissions. Therefore, accurate measurement and analysis of carbon emissions efficiency and evolution characteristics of China's industrial system is the basis for China to improve carbon emissions efficiency. Based on this, we adopted energy consumption method and input-output method to calculate and analyze the industrial carbon emissions efficiency and evolution characteristics of China from 2002 to 2015. The results show that (1) If carbon emissions from cement production are ignored and only energy-related carbon emissions are considered, the calculation results of carbon emissions efficiency of heavy industry will be overestimated about 30%. (2) Compared with 2002, China's industrial carbon emissions efficiency increased by about twice in 2015. Specifically, the optimization of carbon emissions efficiency in agriculture, construction, light industry and service industry is very obvious, which has increased by 5.65, 5.00, 4.69 and 4.68 times respectively; The optimization of carbon emissions efficiency in power sector, heavy industry, transportation and chemical industry was obvious, which increased by 2.55, 2.01, 1.86 and 1.47 times respectively; The carbon emissions efficiency of the fossil energy sector has decreased by 0.36 times. (3) The carbon emissions efficiency of various industries has significant differences. Among them, agriculture, service industry, light industry and construction industry are generally industries with higher carbon emissions efficiency, while power sector, fossil energy sector, transportation industry, chemical industry and heavy industry are generally industries with lower carbon emissions efficiency, especially power and fossil energy sectors. Based on these, this paper provides policy implications and scientific evidence for accurately improving carbon emissions efficiency from the perspective of carbon emissions efficiency. [ABSTRACT FROM AUTHOR]

Combined decomposition of relative productivity growth and absolute changes in input/output variables helps to identify the driving forces of energy conservation and emission reduction. This paper proposes a framework based on the Luenberger productivity indicator (LPI) and the logarithmic mean Divisia index (LMDI) approach to analyze the energy–economy–environment nexus. The empirical case of the China's industrial sector for 2006–2014 is analyzed. Following this approach, we present a variable‐specific decomposition framework which attributes the overall productivity change and its sources to individual input/output variables. Empirically, we examine the relationships between the productivity growth and energy consumption within the LPI–LMDI framework across China's province‐level regions. The results show important spatial variation in the productivity measures. The LPI–LMDI decomposition implies that the industrial energy consumption increased with productivity growth appearing as the only mitigating factor. As regards the industrial SO2 emission, the individual productivity growth effect and the efficiency change effect remained as suppressing factors for emission abatement. These results can be used for evidence‐based decision making. [ABSTRACT FROM AUTHOR]

In order to explore the comprehensive assessment method of air quality of pollutant emission of energy consumption in China's urban internal industrial parks, based on the full consideration of the actual situation of air pollution control in Nanjing HX Industry Park, this paper selects 15 air quality assessment indicators. According to the national air quality standard, the air quality standard of Nanjing and the air quality standard of Nanjing HX Industry Park, the air quality assessment standard is determined about pollutant emission of energy consumption in Nanjing HX Industry Park. Through the technical treatment of the selected assessment indicators, the niche suitability model is introduced, and based on this, the spatial niche suitability model is reconstructed to comprehensively assess the air quality of pollutant emission of energy consumption in Nanjing HX Industry Park. The study found that the air quality of Nanjing HX Industry Park has been improved from level III in 2012 to level I in 2020. According to the assessment results, the differences of assessment results of different niche suitability models are discussed, which fully reflects the advantages of spatial niche assessment methods. The change law and influencing factors of the assessment results are further discussed. [ABSTRACT FROM AUTHOR]

As industry is the world's leading carbon emitter, promoting industrial carbon reduction is of key significance to carbon peak and carbon neutrality. Using a data-driven method, based on the collection and processing of relevant data from statistical yearbooks and others, we analyze the efficiency and amount of carbon emission of each industrial sector after processing multi-dimensional data by the improved IPCC EF method of calculating carbon emissions. In addition, we adopt the LMDI decomposition method for data modeling to measure the contribution of energy efficiency, industrial structure, GDP per capita, and population size to carbon emission changes, to identify targets for industrial carbon reduction, and to propose a targeted optimization path for carbon emission. We show how the method is implemented by taking the statistics of Anhui Province from 2010 to 2019 as an example and advises on an optimization path for carbon emission in Anhui Province. This study is of both theoretical and practical significance as it provides theoretical and methodological support for the low-carbon development of the regional industry, and provides a reference for other countries and regions to explore the path of low-carbon and environment-friendly green transformation and upgrading. [ABSTRACT FROM AUTHOR]

Based on the logarithmic mean Divisia index (LMDI) approach, this paper presents a decomposition analysis of China's energy-related industrial CO2 emissions from 1985 to 2007, as well as a comparative analysis of differential influences of various factors on six sectors. Via the decomposition, five categories of influencing factors are included: (1) Per capita GDP (PCG) was the largest positive driving factor for industrial CO2 emissions growth for all sectors in China, with the largest cumulative contribution value; Population (P), economic structure (YS) and energy structure (ES) also played a positive driving role, but with weak contributions. As the only negative inhibiting factor, energy intensity (EI) significantly reduced the energy-related CO2 emissions from industrial sectors. Meanwhile, CO2 emissions reduction based on the efficiency of energy use still held a large space. (2) Various influencing factors imposed differential impacts on CO2 emissions of six sectors. [ABSTRACT FROM AUTHOR]

Over the past decades, China's vast regional disparities in resource endowment, economic development, and industrial structure have driven large-scale relocations of high energy-consuming industries (RHEIs). Despite extensive research on broad-based industrial relocation and emission transfer, the trend, structure and environmental impact of China's RHEIs need further investigation. This paper employs the multi-regional input-output model (MRIO) to calculate the trend, scale, and structure of the RHEIs among China's 30 provinces. Additionally, we investigate the RHEIs' effect on green total factor energy efficiency (GEE) and its heterogeneity. Our results indicate that the net inflow of high energy-consuming industries decreases GEE, with a more noticeable effect observed in central provinces and after the execution of the Belt and Road Initiative (BRI). However, the positive effect of RHEIs on GEE in outflow areas is more substantial than their negative impact in inflow areas, suggesting that RHEIs can enhance overall efficiency. This study contributes to the understanding of China's industrial relocation by providing valuable insights into China's RHEIs. • China has a large-scale relocation of high energy-consuming industries. • The relocation of China's high energy-consuming industries is calculated. • The relocation's impact on green total factor energy efficiency is analyzed. • Regional heterogeneity is found among different provinces. [ABSTRACT FROM AUTHOR]

Carbon peaking and carbon neutrality goals are proposed by the Chinese government, which aims to reach the highest level of CO 2 emissions by 2030 and achieve carbon neutrality by 2060. It is of great significance to accelerate green production and lead high-quality development. Energy carbon emissions account for 88% of total CO 2 emissions, while forest carbon sequestration account for 80% of China's terrestrial ecosystems. In this paper, we analyzed the spatial and temporal evolutions of industrial energy consumption carbon emissions and forest carbon sequestration of Hubei Province, then demonstrated the offsetting effect of forest carbon sequestration on the industrial CO 2 emissions. Our results documented energy carbon emissions and forest carbon sequestration in Hubei Province both increased from 2000 to 2020, with the growth rates of 4.9423 Mt/yr and 0.28015 Mt/yr. Forest carbon sequestration could offset energy carbon emissions before 2005, while the offsetting effect was weak due to the continuous increase in industrial energy consumption after 2005. Significant spatial heterogeneity was observed in both energy carbon emissions and forest carbon sequestration. Wuhan had the biggest carbon emission with annual average carbon emission of about 20.046 Mt, while Enshi had the biggest carbon sequestration with annual average forest carbon sequestration of about 14.411 Mt. Spatial autocorrelation between energy carbon emissions and forest carbon sequestration was significant in Hubei Province in the past two decades. Our findings provided evidence that local-scale forest carbon sequestration could offset carbon emissions caused by industrial energy consumption at a certain extent, helping to draw up the scientific energy-saving and emissions-reducing measures. [Display omitted] [ABSTRACT FROM AUTHOR]

There has always been controversy over how renewable energy technologies can play a role in reducing carbon emissions. Based on the energy patent data and the economic data of 244 prefecture-level cities from 2007 to 2017 in China, we explore the carbon reduction effect of renewable energy technology and its mechanism from the perspective of energy production, conservation, and management. The two-way fixed effect, instrumental variable, spatial Durbin, and mediation effect models are employed to explore empirical results. We found that (1) the impact of renewable energy technologies on carbon emissions is nonlinear, with an inverted U shape. However, this inverted U-shaped relationship only exists locally in cities and there are uncertainties in adjacent cities, which indicates that cross-regional cooperation in renewable energy technology needs to be improved. (2) The mechanism analysis shows that industrial agglomeration and energy consumption scale are the channels that renewable energy technologies affect carbon emissions. Thus, the implicit carbon emissions generated by industrial agglomeration and the failure to green upgrade energy consumption are the main reasons for the inverted U-shaped relationship. (3) The carbon reduction effect of renewable energy technologies of conservation type takes effect first, and renewable energy technologies of production type do not reduce carbon emissions in non-eastern cities, which means that non-eastern cities are likely to become pollution havens. This study provides evidence for renewable energy technologies to achieve efficient carbon emission reduction and cross-regional technical cooperation. [ABSTRACT FROM AUTHOR]

An improvement in industrial carbon emission efficiency is crucial for achieving both reductions in carbon emissions and sustainable economic growth. In this paper, we use an improved non-radial directional distance function (NDDF) to construct a new meta-frontier total-factor carbon emission efficiency index ( TCEI ) with which we estimate the meta-frontier TCEI of China's 30 provincial industrial sectors in 2005–2015 and analyze their dynamic evolution. The results show that compared to traditional NDDF, the improved NDDF has more advantages in measuring both carbon emission efficiency and the technology gap ratio. For the study period, China's industrial meta-frontier TCEI is low, indicating that the industrial TCEI of many provinces still has much room for improvement. The meta-frontier TCEI has significant inter-group heterogeneity, with Eastern China having the largest carbon emission efficiency, followed by Central China, and Western China having the lowest. China's industrial meta-frontier TCEI increased significantly during the study period with technical progress playing a major role in promoting it. Over time, however, the meta-frontier TCEI growth rate decreased significantly as the deterioration in technological efficiency and the expansion of the technology gap have jointly inhibited the growth of carbon emissions efficiency. Carbon emission performance in various regions over different periods exhibit differing characteristics, that is, the carbon emission performance has significant spatial heterogeneity and period heterogeneity. [ABSTRACT FROM AUTHOR]

Analyzing the driving factors of PM2.5 pollution in different industries is of great significance for developing energy conservation and emission reduction policies in China's industries. In this study, the consumption-based PM2.5 emissions of China's industries are estimated by using an input–output model; on this basis, the generalized Divisia index method (GDIM) is used to measure the contributions of driving factors to the changes in PM2.5 emissions from China's six major industries. The results show that China's consumption-based PM2.5 emissions presented a downward trend from 2007 to 2015, the changes in industrial PM2.5 emissions had a much higher impact on China's total PM2.5 emissions changes than other industries and occupied a dominant position. The generalized Divisia index decomposition analysis results show that investment, output and energy consumption scale were the primary contributors to the increase of PM2.5 emissions in six sectors, with investment scale contributing the most. The investment PM2.5 emission intensity, output PM2.5 emission intensity and energy consumption PM2.5 intensity play a major role in suppressing PM2.5 emissions, while investment efficiency and energy intensity have a smaller inhibitory effect. Therefore, the government should guide investments to more high-end, low-emission industries and encourage companies to increase green investments and use renewable energy and clean energy. Avoiding excessive investments and improving investment efficiency in related industries can also effectively alleviate PM2.5 emissions. [ABSTRACT FROM AUTHOR]

To mitigate the climate change and reduce energy consumption, the Chinese government has applied a variety of polices during the last two decades. Based on data from Industrial enterprises emission and pollution database (IEPED) and Annual Survey of Industrial Firms (ASIF), this study takes Energy Saving and Emission Reduction Plan (ESER) since 2006 as a policy shock to explore the impact of the ESER on industrial firms' energy consumption behavior. The result shows: Firstly, ESER encourages industrial firms to consume less coal, petroleum and natural gas on average. Secondly, by dividing the firms into several groups by the ownership structure, the firm size and the firm age, this paper finds different firms have different strategies in terms of changing their energy consumption to respond ESER. For example, public firms work harder on reducing coal and petroleum consumption per production while private firms prefer to use more natural gas per production. Thirdly, the dynamic effect analysis portrays that traditional fossil fuel intensity declines immediately after energy conversation policy, while the negative effect on industrial firms' natural gas intensity could occur with some delay. Lastly, in terms of total energy consumption, average coal and petroleum consumption of polluted industries and that of non-polluted industries have no difference after the policy. In addition, ESER only has a short-term effect on energy consumption. These results are helpful in understanding how to design an efficient environmental regulation system and protect the environment simultaneously. • This study explores the impacts of the Energy Saving and Emission Reduction Plan (ESER) on firms' energy consumption behavior based on industrial firm data. • ESER encourages firms to consume less traditional energy (coal and fossil fuels) and clean gas. For different firms, their energy intensity changes distinctively in response to ESER. • The dynamic effect analysis portrays that traditional fossil fuel intensity declines immediately after energy conversation policy, while the negative effect on clean gas intensity occur with some delay. • ESER has a short-term and lagged effect on total energy consumption. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims to identify the main drivers behind energy intensity changes of the Jordanian industrial sector and to introduce the impact of energy efficient measures within the Jordanian industrial sector. To achieve these objectives, two empirical models were developed for electricity and fuel intensities, respectively of the Jordanian industrial sector based on multivariate linear regression. It was found that the structural effect, electricity prices, capacity utilizations and number of employees are the most important variables that affect changes of electricity intensity while fuel prices, capacity utilizations and number of employees factors are the most important variables that affect fuel intensity. The results show that multivariate linear regression model can be used adequately to simulate industrial energy intensity with very high coefficient of determination. Also, the impact of implementing energy saving technologies, such as use of high efficiency motors (HEMs), optimize motor size, variable speed drives (VSDs), bare steam pipes insulations, steam leak prevention, steam traps repair, and adjustment of boiler air/fuel ratio were investigated and found to be significant. Without such basic energy conservation and management programs, energy consumptions and associated GHG emissions for the industrial sector are predicted to rise by 25% and 23%, respectively in the year 2021. If these measures are implemented on a gradual basis, over the next decade, industrial energy consumption is predicted to rise at a lower rate, reaching 11.9% for same period with low/no cost actions. This would yield an estimated annual emission reductions of 570×103 t. In addition, the total installed capacity cost savings is estimated to be around 81.9 million US$ by year 2021. [Copyright &y& Elsevier]

Abstract: Knowledge of influencing factors of industrial carbon emissions (ICE) is crucial to the efforts of reducing anthropogenic greenhouse gas emissions. In this paper, main factors responsible for the ICE in Shanghai between 1996 and 2007 were identified and quantitatively analyzed using the Log-Mean Divisia Index method. It was found that the industrial output was the main driving force of ICE. The decline in energy intensity and the adjustment of energy and industrial structure are major determinants for reduction of ICE, with the former alone accounting for 90% of the reduction. To better investigate the relative contribution of different industrial sectors and their changes over time, we divided the study period into two equal time intervals and analyzed some high-carbon emission sectors. The results suggested that the intensity of energy use should be reduced further, for it was far higher than the world average. Adjustment of industrial structure by developing low-carbon emission industries is more crucial than energy mix. [Copyright &y& Elsevier]

PM 2.5 emissions has become a serious issue in China with enormous regional variation in economic development, resource endowment, and consumption patterns, which leads to great variations in regional PM 2.5 emissions and embodied PM 2.5 transfer among provinces. The spatial and temporal evolution pattern of PM 2.5 and its influencing factors are important issues in the study of air pollution emissions and control, which could help policy makers allocate emission responsibilities and reduction targets. Based on the perspective of final consumption, this paper uses a multiregional input-output model to estimate PM 2.5 emissions in 30 provinces in different years in China, applies a geographically weighted regression model to analyze different driving factors, and then analyzes the spatial heterogeneity of consumption-based PM 2.5 emissions. The results show that consumption-based PM 2.5 presents a significant reduction trend, although the separation of production and consumption areas of PM 2.5 emission is further intensified. The provinces with rich energy but relatively backward economies and manufacturing industries correspond to the primary net inflow of PM 2.5 , while the provinces with strong consumption ability and the most developed economies and industries present a net outflow of PM 2.5. Moreover, the slowdown of consumption growth and the improvement of consumption quality have a positive impact on PM 2.5 emission reductions. The optimization of industrial structure has a more significant impact on pollution control and haze reduction in the western region, while the improvement of energy utilization efficiency and rational use of construction land are more conducive to the reduction of consumption-based PM 2.5 emissions in the eastern region. From the perspective of temporal and spatial changes, significant spatial differences occur between regions. Energy consumption, environmental protection investment and construction land show the trend of " high value area diffusion and low value area reduction". • Explores the spatial distribution characteristics of PM 2.5 emissions in China from the perspective of consumption. • Consider and analyze the inter-annual variation of pollutant emission transfer. • Clarifies the spatial heterogeneity characteristics of influencing factors. • Emphasizes the importance of further optimizing influencing factors to reduce haze from the consumption end. [ABSTRACT FROM AUTHOR]

• Energy & environmental inefficiency of the 45 cities in China is analyzed. • Variable-specific productivity growth measures are used. • Bounded-adjusted Measure and Luenberger Productivity Indicator are applied. • Dagum Gini coefficient is employed to measure the regional disparities of energy use. • Policy guidelines are presented based on the productivity change directions. Analyzing green transformation of energy use and pollutant emissions in China's "Three Regions and Ten Urban Agglomerations" (TRTAs) allows effectively promoting sustainable development in the country. This paper applies Data Envelopment Analysis, namely the Bounded-adjusted Measure (BAM) relying on the additive structure, to measure the technical inefficiency and productivity change across TRTAs in China. The technical inefficiency and productivity change observed for TRTAs are 0.29 and 2.29% respectively. The decomposition results indicate that industrial energy consumption, industrial sulfur dioxide (SO 2), and industrial soot & dust emissions are the main variables causing TRTAs' industrial operation inefficiency. Spatially, environmental inefficiency of the above-mentioned three variables in North China Urban Agglomeration (NA) is higher than those in Northwest, Yangtze and Pearl River Delta Urban Agglomerations (NWA, YRDA, and PRDA respectively). Decomposition of the Gini coefficient shows that the performance associated with the three variables varies among the regions with intra-regional differentiation of the PRDA being the highest. Given the regional patterns in productivity change, more efforts should be directed towards improving technical progress on industrial energy conservation in NWA. Furthermore, regulations on industrial air pollutant emissions, joint mitigation and monitoring of the key indicators in PRDA should also be emphasized. [ABSTRACT FROM AUTHOR]

The Environmental Kuznets Curve (EKC) is an important tool to analyze the relationship between environment and per capital income. But two significant issues have been overlooked: why some points are above the EKC while others are below it? And what factors caused this phenomenon? Based on the data of 30 provinces in China over 2005–2016, this paper aimed to solve the above two questions. For the first question, we take the point on EKC as the theoretical pollution emission value of the different economic development stages; and use the difference between the theoretical pollution emission and the actual pollution emission under the same per capita income as the pollution emission efficiency. The difference value is used to reflect whether the pollution emission of the corresponding economic development stage of each region exceeds the standard and by how much. The result shows that most of the provinces with low pollution emissions concentrated in the north of the Yangtze River. Great progress has been made in China's pollution reduction work during 2015–2016. Then, for the second question, through the analysis of multiple mediating effects, we find that Environmental regulation, industrial transfer, proportion of output value of heavy pollution industry, energy consumption structure and energy using efficiency are important determinants to the pollution emission efficiency in Chinese province. [ABSTRACT FROM AUTHOR]