Carbon Emission Efficiency Research Articles

The impact of internet development on carbon emission efficiency: Evidence from China

Modern information and communication technology presented by the Internet has become the significant driving force to promote the realization of “carbon peak and carbon neutrality” target in China. Taking China’s 30 provinces over the period 2006–2019 as research object, this paper investigates the effect of Internet development on carbon emission efficiency (CEE) and its impact mechanism by using the dynamic spatial Durbin model (DSDM) and mediation effect model. The regression results indicate that: (1) Internet development can significantly improve the carbon emission efficiency in the region and its neighboring areas. (2) The positive effect of the Internet development on CEE in long-term is greater than that in short-term. (3) Internet development can directly improve CEE, as well as indirectly increase CO2 emission efficiency by promoting technological innovation and industrial structure upgrading, as well as reducing the level of labor and capital resource mismatch. The conclusions of this paper are reliable after multiple robustness checks. Finally, we have put forward several policy implications to be conducive to achieving carbon neutrality.

Does larger scale enhance carbon efficiency? Assessing the impact of corporate size on manufacturing carbon emission efficiency

This study investigates the impact of corporate scale on manufacturing corporate carbon efficiency (MCEE) using empirical analyses and diverse modeling techniques. The research begins with rigorous unit root and cointegration tests, confirming the stationary nature of the data and establishing long-term equilibrium relationships among the variables. Subsequently, benchmark regression analyses employing various models, including system GMM, reveal a robust and significantly positive association between corporate scale and MCEE. The findings emphasize that as corporate scale increases, there is a substantial enhancement in MCEE, attributed to expanded resources, technological advancements, and improved management systems. Robustness tests employing alternative approaches consistently reaffirm this positive correlation, further validating the pivotal role of corporate scale in augmenting MCEE. Furthermore, nonlinear analyses demonstrate varying impacts across different quantile levels of MCEE, highlighting the consistent positive correlation between corporate scale and MCEE. Additionally, the panel threshold model underscores the influence of environmental regulations, R&D investments, and trade openness on this relationship. Notably, stricter environmental regulations intensify the impact of corporate scale on MCEE, emphasizing the importance of corporate expansion in minimizing environmental costs and enhancing resource efficiency. These findings underscore the significance of corporate scale in driving corporate carbon efficiency. They advocate for corporations to not only expand their production capacities but also focus on optimizing management strategies and resource allocation, particularly in contexts influenced by environmental regulations, R&D investments, and trade openness. Overall, this research contributes comprehensive insights into understanding the dynamics governing corporate environmental efficiency and emphasizes the critical role of corporate scale in fostering environmental sustainability.

The Impact of Digital Economy Development on Carbon Emission Efficiency in the Logistics Industry

The Impact of Digital Economy Development on Carbon Emission Efficiency in the Logistics Industry

Mitigating the disparities in carbon emission efficiency enhancement in China's coal resource-based cities

Enhancing carbon emission efficiency in coal resource-based cities (CRBCs) is pivotal for energy conservation and carbon reduction, but the combined pressures of technical efficiency and technological innovation may drive a sharp disparity in the sustainability of carbon emission efficiency enhancement across such cities. Here, we develop a “Static-Dynamic-Synergistic” progressive assessment framework, to systematically assess the carbon emission efficiency of 51 CRBCs in China from 2000 to 2020. We found that the carbon emission efficiency of CRBCs exhibited a fluctuating upward trajectory, with the average efficiency rising from 0.40 to 0.64. The enhancement of carbon emission efficiency in CRBCs has shifted from relying solely on efficiency change (EC) to incorporating both EC and technology change (TC). TC, EC, and the Carbon Emission Efficiency Improvement Synergy Index (CEEISI) exert a substantial positive influence on the increment of carbon emission efficiency. While the CEEISI of maturity CRBCs has increased by 2.22 % over the past 15 years, varying declines are observed for other types of CRBCs. CRBCs should formulate adaptive policies based on the stage of city development, fostering socio-economic-ecological transformation, synergistically improve technical efficiency and technological innovation, thereby achieving sustainable enhancement in carbon emission efficiency.

Can internet development improve carbon emission efficiency for manufacturing? The role of market integration

Improving carbon emission efficiency of manufacturing is important for promoting low-carbon development and achieving harmonious coexistence between humans and nature. In this study, we use the Global-MinDS model to measure the carbon emission efficiency of China's provincial manufacturing from 2011 to 2020. To do so, we construct a mathematical model to analyze the mechanism of the interaction among internet development, market integration, and carbon emission efficiency of manufacturing. Then, we empirically examine the carbon emission reduction effect of internet development using the Bartik instrument variable method and verify the significance of market integration. Our results show that internet development can significantly improve the carbon emission efficiency of manufacturing, promoting manufacturing low-carbon transformation. Carbon emission reduction effect of internet development is characterized by heterogeneity in market integration, environmental regulation, and policy intensity. This means that the impact of internet development is more pronounced in lower integration regions, higher environmental regulation intensity and in big data comprehensive experimental zone. Intermediate mechanism tests show that internet development can promote market integration by reducing external transaction costs, so as to play a carbon emission reduction effect in the market mechanism. Our study provides empirical evidence for the carbon emission reduction effect of the digital economy and offers valuable references for effectively promoting the low-carbon transformation of manufacturing in China from the perspective of unified market.

Prediction of national carbon emission efficiency based on ARMI-Convolution LSTM*

Prediction of national carbon emission efficiency based on ARMI-Convolution LSTM*

Coupling and Coordinated Development of Carbon Emission Efficiency in Industrial Enterprises and the Digital Economy: Empirical Evidence from Anhui, China

To delve into the interrelationship between the green transformation of industry and the economy’s high-quality development, to promote the coordinated development of industrial carbon emission efficiency and digital economy, to expand the scope and research ideas related to economic and social sustainable development, and to provide scientific reference for the low-carbon sustainable development of regional economy, this article introduced a data-centric methodology for evaluating the collaborative advancement of both industrial enterprises’ carbon emission efficiency and the digital economy. To accurately gauge the carbon footprint of industrial enterprises, models focusing on carbon emissions as well as carbon emission intensity were employed. To enhance the precision of evaluation outcomes and mitigate biases stemming from subjective weighting factors, we employed the entropy weight method to objectively assign weights to each indicator. Furthermore, the super-efficient slack-based model (SBM) can solve the problem that the conclusions are biased, due to the different radial. Subsequently, a carbon-emission efficiency slack-based measure model, and models for coupling degree and coupling-coordination degree were formulated. Anhui, as a central province in China, is also an important province in the Yangtze River Delta integration development. Coordinated development of its carbon emission efficiency and digital economy has important implications for the sustainable economy advancements of other regions in China, and even other countries or regions in the world. Therefore, Anhui was selected to be the empirical research sample. The results showed that the comprehensive levels of these two systems followed an increasing trend, while the digital economy lagged. Their coupling degree fluctuated and reached its highest point in 2021, whereas their coupling-coordination degree increased, showing high coupling and low coordination overall. This study proposes specific countermeasures and suggestions for the relevant decision-makers.

Study on stochastic convergence of China’s carbon emission efficiency

ABSTRACT Carbon emission efficiency (CEE) and its stochastic convergence are important indicators for achieving the strategic goal of carbon peaking and carbon neutrality. Thus, this paper firstly estimates the CEE of 30 provinces in China from 1995 to 2019 with the super efficiency of the SBM model. Then the panel unit root test method is adopted to test its stochastic convergence. The results show that the overall CEE has not reached the effective level, and the CEE level varies greatly among regions. There is a stochastic divergence of CEE in most regions from 1995 to 2001. While the CEE of most regions changes to convergence after 2002. From 1995 to 2019, the CEE of the western, northeast, and southwest regions diverge. While the CEE of the national level, the eastern, central regions, and the other six regions in the eight major economic zones converged stochastically. Among the divergent areas, the CEE of Heilongjiang, Guangxi, and Sichuan provinces are the main contributors to the CEE divergence. Therefore, it is the key to narrowing the differences in regional low-carbon development in China by significantly improving the CEE of each province.

Information disclosure, multifaceted collaborative governance, and carbon total factor productivity—An evaluation of the effects of the ‘environmental information disclosure pilot’ policy based on double machine learning

As an environmental institutional arrangement related to the information factor of the diversified participation of the government, enterprises, the media and the public, the environmental information disclosure pilot policy, can and how to affect the carbon emission efficiency through multiple collaborative governance? This study uses the Environmental Information Disclosure Pilot Policy implemented in China in 2007 as a quasi-natural experiment. It examines 284 prefecture-level cities from 2004 to 2021 and A-share listed companies from 2004 to 2021, constructing an evolutionary game dynamic model involving government, public, enterprises, and media. Through mathematical derivation and assignment analysis, it explores how environmental information impacts carbon emission efficiency under multifaceted collaborative governance, assessing the strategic choices and evolutionary paths of stakeholders before and after policy implementation, using methods like double machine learning for empirical testing. The study highlights several key findings: First, the implementation of the Environmental Information Disclosure Pilot Policy significantly enhanced carbon total factor productivity in pilot cities, as revealed through Double Machine Learning (DML) policy effect evaluation. Second, adjustments for potential estimation biases using Doubly Debiased LASSO (DDL) regression indicated that environmental information disclosure impacts carbon productivity via a governance mechanism involving government, public, media, and enterprises. Third, a causal pathway analysis suggested a sequential logic in governance effectiveness, starting from governmental environmental focus to corporate environmental responsibility. Lastly, integrating DML with a moderation effect model revealed a regulatory role for environmental legislation construction, offering new insights for achieving dual carbon goals and enriching empirical evidence on information's impact on carbon emission efficiency.

Coupled coordination and pathway analysis of food security and carbon emission efficiency under climate-smart agriculture orientation

Owing to the contradiction between agricultural production and environmental development, the issues of food security and carbon mitigation cannot be isolated, and achieving coupled and coordinated development is the key to agricultural sustainability. This study adopted the coupled coordination model and dynamic qualitative comparative analysis (dynamic QCA) method to measure the coupled coordination degree (CCD) of the food security index (FSI) and agricultural carbon emission efficiency (ACEE) in 31 provinces of China from 2010 to 2021, seeking paths to achieve high coupled coordination from Climate-Smart Agriculture technology, external environment, and incentive dimensions, and simulating path selection differences under various CSA priority scenarios. The results indicated that the CCD of the FSI and ACEE in China significantly increased year-on-year increase, with significant regional differences primarily reflected in the Northeast > East > West > Central regions. Based on the CSA orientation, the “technology-environmental safeguard” linkage path and the “technology-environment-incentive” hybrid path are proposed. There are differences in CSA practices across regions, which require customization based on their unique socioeconomic, ecological, and political landscapes. When priorities favour food security, the “technology-environment-incentive” hybrid pathway supports high CCD, and as priorities increase, the contribution of CSA technologies, centred on water-saving irrigation, increases and the role of the external environment diminishes. When the priority tendency is to mitigate emissions, both paths can achieve high CCD. As the priority tendency for carbon emissions increases, urbanisation and CSA technologies such as water-saving irrigation and straw return become essential factors contributing to higher coupling coordination, and the role of agriculture-related financial expenditures diminishes. These findings provide policy support for safeguarding food security and low-carbon agriculture.

Allocation of CO2 emission quotas for industrial production in Industry 4.0: Efficiency and equity

The method of carbon emission quota (CEQ) allocation based on data envelopment analysis (DEA) can allocate quotas without impacting efficiency improvements in industrial sector. A key element in effectively reducing carbon emissions is ensuring accurate efficiency measurement and equitable allocation of CEQ in the Industry 4.0 era. This paper divides industrial system into two stages: production (which more accurately reflects carbon emissions in production) and trading (which involves in the commercial system). Carbon emission efficiency in the production stage is calculated with a DEA model, with a high degree of accuracy. Given that higher efficiency values correspond with stronger capacity to bear the costs of carbon reduction, a CEQ allocation model is proposed which considers the contribution degree of efficiency. This model is applied to China’s industrial production system. The results show that a CEQ allocation scheme that focuses only on the production stage is more equitable than schemes which treat the industrial system as a black box. In allocation schemes which focus only on the production stage, the central region of China is least able to bear the cost of carbon reduction and receives the most CEQ, and the opposite is true in the eastern region of China. The proposed method not only measures the carbon emission efficiency of industrial production more accurately, but also considers the balanced development of different regions when allocating carbon emission rights. The CEQ allocation scheme based on our model can confidently meet the needs of sustainable development in the era of Industry 4.0.

Industrial co-agglomeration, green finance and urban carbon emission efficiency: Empirical evidence from Shandong province in China

<p class="MsoNormal" style="line-height:200%"><a name="_Hlk156583416"><span lang="EN-US" style="font-size:12.0pt;line-height:200%;font-family:"Times New Roman",serif">This study explores the impact of </span></a><a name="_Hlk156583477"><span style="mso-bookmark:_Hlk156583416"><span lang="EN-US" style="font-size:12.0pt; line-height:200%;font-family:"Times New Roman",serif">industrial co-agglomeration</span></span></a><span style="mso-bookmark:_Hlk156583416"><span lang="EN-US" style="font-size:12.0pt;line-height:200%;font-family:"Times New Roman",serif"><span style="mso-spacerun:yes">  </span>on urban carbon emission efficiency in Shandong Province from China during 2007 to 2020. The moderating effect model is constructed to explore the role green finance in the impact of industrial co-agglomeration on urban carbon emission efficiency. </span></span><span lang="EN-US" style="font-size:12.0pt;line-height:200%;font-family:"Times New Roman",serif">We explore the influence mechanism of industrial co-agglomeration on urban carbon emission efficiency.<a name="_Hlk156583540"> Through conditional process analysis, we </a><a name="_Hlk156584855"><span style="mso-bookmark:_Hlk156583540">investigate</span></a><span style="mso-bookmark:_Hlk156583540"> the moderating effect of green finance in the mechanism of industrial co-agglomeration on </span>urban carbon emission efficiency. Empirical results indicate that: (1) The impact of industrial co-agglomeration on urban carbon emission efficiency is inverted U-shaped. (2) <span style="mso-spacerun:yes"> </span>Green finance has a positive moderating effect in the impact of industrial co-agglomeration on urban carbon emission efficiency. (3) Industrial co-agglomeration affects urban carbon emission efficiency through scale effect, technological effect and structural effect. (4) Green finance plays a moderating role in the impact pathways of industrial co-agglomeration on urban carbon emission efficiency.<span style="color:red"> </span>Therefore, this paper proposes <a name="_Hlk137756237">targeted recommendations</a> to improve urban carbon emission efficiency according to industrial co-agglomeration and green finance development.<o:p></o:p></span></p>

Does Rural Labor Transfer Impact Chinese Agricultural Carbon Emission Efficiency? A Substitution Perspective of Agricultural Machinery

In this paper, referring to Kaya’s method, the ratio of gross agricultural production (GAP) and agricultural carbon emission is defined as agricultural carbon emission efficiency (ACEE). Considering rural labor transfer (RLT) increases the agricultural machinery intensity (AMI), the two are substitutes for each other and may interact with agricultural carbon emission efficiency (ACEE). We constructed a Simultaneous Equations Model (SEM) of ACEE, RLT and AMI and analyzed the interaction mechanism of these three variables using the Three-Stage Least Squares (3SLS). The following conclusions are drawn. First, RLT and AMI significantly promote the improvement of ACEE, while the improvement of ACEE and AMI further promotes RLT. Secondly, the causal relationship and influence mechanism of ACEE, RLT and AMI are interactive and multi-directional. For example, an increase in AMI promotes ACEE, but an increase in ACEE inhibits an increase in AMI. Finally, China has significant regional heterogeneity, and different regions have different interaction mechanisms. Local governments should consider the local economic base and technological level when implementing policies. This paper extends the analytical framework of ACEE, RLT, and AMI and provides a reference for governments to make policies.

Can industrial intelligence promote carbon emission efficiency? --empirical research based on the Yangtze River Economic Belt

The rapid development of intelligent technology characterized by robots under the fourth scientific and technological revolution provides a favorable opportunity for the accurate decision-making of urban pollution control and the effective achievement of the goal of carbon emission reduction in China. This research uses the robot penetration rate as a representative and characteristic index of industrial intelligence development, based on panel data from 108 cities in the Yangtze River Economic Belt (YEB) from 2006 to 2020. It then uses panel quantile regression, spatial measurement, and threshold effect models to provide a more thorough theoretical and empirical discussion of the impact, mechanism, and spatial effect of industrial intelligence development on urban carbon emission efficiency (CEE). Urban CEE may be greatly increased with the use of industrial intelligence, and this finding remains true even after endogeneity and robustness tests are controlled; From an action mechanism perspective, industrial intelligence advances technology, optimizes industrial structure, and ultimately enhances regional CEE; There is a Matthew effect on the degree of development of carbon emission efficiency, and the impact of industrial intelligence on CEE is more pronounced in non-resource-based cities and the lower portions of YEB; Urban CEE increases positively with the spatial spillover impact of industrial intelligence development. The ability for regional sustainable development will be significantly increased if cross-regional cooperative prevention and control of environmental governance can be successfully achieved. This study verifies the enabling effect of industrial intelligence development on the improvement of urban CEE, and provides enlightenment for China to improve industrial intelligence development strategies and policies to achieve regional high-quality development.

A Bottom-Up Carbon Emission Assessment Model for Carbon Emission Control at the Level of Rural Detailed Planning

Incorporating green and low-carbon building targets into the rural planning management system requires scientific and quantitative methods for assessing carbon emissions from rural land use. At present, the research in this field mainly focuses on urban areas, but there are fewer theoretical and practical studies on the assessment of carbon emissions from rural land use. This paper proposes a new carbon emission assessment method based on land use modes, and the model can not only assess carbon emissions but also directly reflect the carbon emission intensity of different land use spaces in rural areas and guide the carbon emission control of construction land in village planning. In this paper, we take suburban rural areas in Hunan Province as an example and establish a land use carbon emission assessment model with 13 indicators in five dimensions: total carbon emission, carbon emission efficiency, carbon emission intensity per unit of land use, carbon emission structure of land use, and carbon emission intensity of buildings, based on the bottom-up field research data. We practised our method in Jinlong Town, Hunan Province, and gave examples of model applications. It was found that the carbon emission calculation method based on the carbon emission intensity of land use can be used to calculate the current status of carbon emissions in different villages in Jinlong Town. At the same time, the carbon emission assessment results can be used as a scientific basis for carbon emission control in detailed village planning in Jinlong Town. In general, the carbon emission assessment model can complete the assessment of land carbon emissions in rural areas and provide a low-carbon land use management tool for the government.

Retraction Note: How do environmental regulations affect carbon emission and energy efficiency patterns? A provincial-level analysis of Chinese energy-intensive industries.

Retraction Note: How do environmental regulations affect carbon emission and energy efficiency patterns? A provincial-level analysis of Chinese energy-intensive industries.

Can Rural Digitization and the Efficiency of Agricultural Carbon Emissions Be Coupled and Harmonized under the “Dual-Carbon” Goal?

A major driving force behind China’s low-carbon, environmentally friendly development of agriculture and the accomplishment of the “dual-carbon” goal is the digital transformation of rural areas. In this study, on the basis of clarifying the degree of rural digitization and agricultural carbon emissions efficiency in China from 2010 to 2021, the degree of coupled coordination and the spatiotemporal pattern characteristics between the two are examined using the coupled coordination model. Then, the influencing factors are analyzed in depth using the spatial Durbin model. Our findings reveal that, first, in terms of the degree of rural digitization, the index increases overall and the spatial imbalance is obvious, with a spatial distribution pattern of “high in the east and low in the west”. Regarding the efficiency of agricultural carbon emissions, there is an overall “N-shaped” change, which is mainly influenced by technological progress in agricultural production, and the regional annual averages are, in descending order, the Western, Eastern, Northeastern, and Central regions, with obvious regional differences. Second, the coupling coordination index shows a fluctuating upward trend, from “extreme disorder” to “high-level coordination”. Furthermore, there are obvious regional characteristics. The regional growth rates are, in descending order, the Western, Eastern, Central, and Northeastern regions. Third, coupling coordination is jointly influenced by a variety of factors, including government input, educational level, industrial structure, energy use, urbanization rate, living standards, driving temporal patterns, and regional differences. This study not only helps to clarify the relationship between the two, offering a reference for the realization of the “dual-carbon” goal, but also broadens the concepts of the low-carbon and environmentally friendly development of agriculture.

The impact of intellectual property demonstration policies on carbon emission efficiency

Confronted with the concurrent challenges of economic advancement and environmental management, this study explores whether implementing Intellectual Property Demonstration Policies (IPDP) can be a covert force in enhancing carbon emission efficiency. Utilizing panel data from 280 prefecture-level cities in China over the period 2007–2019, we employ a quasi-natural experimental design, incorporating multiple-period difference-in-differences models, mediation effect models, and spatial Durbin difference-in-differences models to assess the impacts of IPDP on carbon emission efficiency, its mechanisms of action, and its spatial spillover effects. The regression results of the multi-period difference-in-differences model reveal a statistically significant enhancement in carbon emission efficiency due to IPDP, with an impact coefficient of 0.044. Through heterogeneity tests, it is observed that the influence of IPDP on carbon emission efficiency varies based on regional characteristics, carbon emission levels, and the extent of marketization. The mediation effect model demonstrates that IPDP enhances carbon emission efficiency by fostering green technological innovation and facilitating the transformation of industrial structures. Furthermore, the spatial Durbin difference-in-differences model illustrates that IPDP positively influences the carbon emission efficiency of neighboring regions, indicating favorable spatial spillover effects. Notably, the indirect effect coefficients in the geographical distance matrix, economic distance matrix, and economic-geographical nested matrix are calculated as 0.673, 0.250, and 0.386, respectively. These findings offer compelling theoretical and empirical support for strengthening the intellectual property framework to optimize its environmental impact.

Carbon emission efficiency and regional synergistic peaking strategies in Beijing-Tianjin-Hebei region

In the context of China's resolute advancement of dual carbon goals (carbon peaking and carbon neutrality), urban agglomerations emerge as pivotal areas for carbon emission mitigation due to their dense economic activities and rapid urbanization. Previous studies overlook regional disparities in carbon emission prediction, disregarding the variations and policy directives across different provinces or cities. Therefore, this study addresses the research gap by investigating synergistic strategies to foster regional carbon peaking within the Beijing-Tianjin-Hebei region. Employing a novel approach tailored to regional segmentation policies, we provide more accurate predictions reflecting real-world conditions and distinct policy landscapes. Meanwhile, we integrate carbon emission efficiency into our analysis, emphasizing the dual goals of emission reduction and quality economic growth. Our empirical investigation in the Beijing-Tianjin-Hebei region, utilizing the Super-SBM and extended STIRPAT models, reveals upward trends in carbon emission efficiency, with varying trajectories across cities. Scenario simulations informed by the "14th Five-Year Plan" demonstrate that under the green development scenario, carbon peaking accelerates, alongside enhanced efficiency, supporting long-term emission reduction. Moreover, we design seven regional synergy carbon peak strategies for scenario simulations to facilitate the rational layout of dual carbon policies for collaborative development. We find that synergistic strategies have proven more effective in reducing regional carbon emission and increasing efficiency than strategies focusing solely on economic development or energy conservation. This innovative finding emphasizes the necessity of comprehensive green development in the Beijing-Tianjin-Hebei region and provides strong evidence for policymakers. Our research contributes to targeted strategies for improving carbon emission efficiency and reducing emissions, emphasizing the importance of synergistic approaches for regional carbon reduction.

Toward a Green Energy System: How Does Carbon Capture, Utilization, and Storage Technology Innovation Promote Green Total Factor Productivity?

Abstract Using a panel data set from 2007 to 2019, we empirically evaluate the impact of carbon capture, utilization, and storage (CCUS) technology innovation on green total factor productivity (GTFP). The findings show that (1) CCUS technology innovation significantly improves GTFP. (2) CCUS technology innovation significantly contributes to GTFP by promoting industrial structure upgrading and carbon emissions efficiency. (3) Environmental regulation plays a positive moderating role in the nexus between CCUS technology innovation and GTFP. The findings of this paper provide guidance for China to achieve green energy transition and build a green energy system.