Measurement of environmental efficiency in the countries of the European Union with the enhanced data envelopment analysis method (DEA) during the period 2005-2012

Can digitalization effectively promote green energy efficiency? The linear and nonlinear relationship analysis

In light of the integration of digitalization and the energy revolution, digitalization can be integrated into the energy industry to develop energy-saving technologies and improve resource allocation efficiency. On the basis of 2013-2019 Chinese provincial panel data, this paper measures the level of green energy efficiency based on the super-EBM-DEA model and analyzes the linear relationship, nonlinear relationship, and potential mechanism between digitalization and green energy efficiency. The findings indicate that (1) overall, both China's digitalization and green energy efficiency formed a steady upward trajectory during the sample period. Digitalization showed a spatial characteristic of extending and spreading from the eastern region to the central and western regions. Green energy efficiency was characterized by obvious regional heterogeneity. (2) Progress in digitalization has a significant driving effect on green energy efficiency. Subdimensional analysis shows that this driving effect mainly comes from digital development and digital transactions. (3) The impact of digitalization on green energy efficiency presents a threshold effect of economic agglomeration (with a threshold of 0.0257 and a marginally increasing, positive driving trend) and population agglomeration (with a threshold of 4.2750 and a marginally decreasing, positive driving trend). (4) Decomposing changes in green energy efficiency into scale efficiency and pure technical efficiency, this study shows that pure technical efficiency gains due to digitalization are the main driver of green energy efficiency improvements. Finally, some specific policy recommendations are proposed.

Application of the data envelopment analysis technique to measure the environmental efficiency of the 27 countries of the European Union during the period 2012-2020

In the last decade, we have experienced a changing geopolitical context that has caused changes in the energy context. In addition, human activity contributes to global warming or sea level rising, i.e., climate change. A set of action policies have been implemented to continue fighting against this environmental situation (such as the Paris Agreement, the COP27, or the European Green Deal for 2030); therefore, it is necessary to determine whether we are on the right track. It is compulsory to develop predictive models that accurately analyze the current status and the already path undertaken. To this end, this article analyzes the environmental efficiency of the 27 countries of the European Union (excluding the UK) using the so-called data envelopment analysis (DEA). In particular, economic (GDP and GDP per capita), environmental (CO2 and CH4 emissions), electricity production data, the volume of vehicles, and the industrial production rate of the different countries were collected to calculate environmental efficiency. Once these data were collected, the environmental efficiency was calculated using two methods based on the DEA. The results show that out of the 27 countries, only 12 have a relatively high environmental efficiency, although it could be improved, implementing a set of corrections. However, other countries have a low eco-efficiency performance and they must improve in the coming years. We can highlight that rich countries are closer to achieving high environmental efficiency than less developed countries.

Graphical Abstract

Political map of the European Union indicating the average eco-efficiency with colors of the 27 countries of the DEA method.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10098-023-02553-9.

Investigating the prospect of cleaner production in informal enterprises: A scientific assessment of environmental burdens and economic efficiency

The present study aims to assess the prospects for cleaner production (CP) and sustainable development (SD) of informally operated small manufacturing enterprises, which are frequently blamed for uncontrolled waste disposal and causing pollution to the environment. The economic efficiency level of these firms has been explored to this end, and the metallic pollution loads in the surrounding environment have been scientifically analyzed to investigate the nexus between these two. DEA (Data Envelopment Analysis)-Tobit analysis has been employed, and a pollution load index (PLI) of heavy metal pollution comprising two environmental compartments (soil and water) has been constructed based on the concentration level of metalloid pollutants in the samples collected from the surrounding areas of the studied informal firms in Bangladesh. The study disproves CP practice in majority of the informal firms in Bangladesh by observing a positive relationship between firm-level efficiency and pollution load sourced from their production activities. Afterwards, this study estimates the eco-efficiency level of firms by considering pollution load as an undesirable output and minimizing its impact in an input-oriented DEA model. Applying the eco-efficiency scores in censored Tobit regression analysis, the outcome endorses the prospect of CP for informally operated enterprises in Bangladesh. However, the CP prospect can only materialize if and only if firms are provided with adequate technical, financial, and strategic support for achieving eco-efficiency in their production. The informal and marginal nature of the studied firms restricts them from getting access to the facilities and support services needed for implementing CP and moving towards sustainable manufacturing. Therefore, this study recommends green practices in informal manufacturing and limiting the informal firms by bringing them gradually under the coverage of formalization, which is in line with the achievement of the targets mentioned in Sustainable Development Goal 8.

The spatial changes of China's environmental efficiency and driving factors from the perspective of circular economy

This paper mainly uses the super-efficiency EBM model to measure the environmental efficiency of 30 provinces in China from 2005 to 2019, utilizes the Theil index to analyze the degree of differentiation, and investigate the stochastic convergence of environmental efficiency in different regions. At the same time, it focuses on exploring the direction, intensity, and changing trend of the internal driving factors of environmental efficiency including fiscal decentralization and tax competition, so as to measure and show the overall situation of circular economy development. The research results show that (1) from a national perspective, environmental efficiency shows a pattern of gradual convergence from east to west and from coast to inland. There is a significant stepped regional imbalance in the development level of circular economy in the eastern coastal areas and the central, western inland areas. (2) The differences in environmental efficiency among the four major economic regions were apparently significant while the differences inside each region itself were relatively minor, although in a trend of being gradually widened. There are differences in the development level of circular economy in different regions or within the same region. (3) The inter-provincial efficiency in the eastern, western and northeastern zones maintained relatively stable, while the inter-provincial differences in the central region were expanding. The environmental deficit problem caused by economic development has been alleviated and the basic development model of circular economy has been initially established. (4) Economic development has played a positive role in improving the environmental efficiency of the region. But the resident consumption level inhibited the improvement of the environmental efficiency level of the surrounding areas. The conclusion of this paper can provide a macroscopic reference for the government in finding effective countermeasures to improve environmental efficiency.

A branch and efficiency algorithm to design a sustainable two-echelon supply chain network considering traffic congestion and uncertainty

This work aims to design a sustainable two-echelon supply chain not only based on the widely used cost perspective, but also based on the efficient use and preservation of limited resources. For this purpose, a branch and efficiency (B&E) algorithm is developed, which includes an optimization model and an evaluation model. The proposed tri-objective optimization model simultaneously minimizes the total cost of the supply chain, maximizes the sustainability score, and minimizes inequity among customers. The solutions obtained from the optimization model are then evaluated by extended data envelopment analysis (EDEA) models based on common criteria (i.e., cost and service) and traffic congestion criterion. To take into account real-world conditions, parameters related to labor and demand are assumed under uncertainty. Since the presented models consist of more than one objective function, fuzzy goal programming (FGP) method is utilized to tread the multi-objectiveness. The obtained results from tackling a case study problem demonstrate that considering sustainability issues can positively affect both the economic and social aspects of the problem. Furthermore, the developed B&E algorithm is able to reduce costs in each iteration; this is what supply chain managers are interested in. On the other hand, this algorithm can provide more services to applicants compared to one of the competing algorithms.

Analysis of the spatial characteristics and influencing factors of agricultural eco-efficiency: evidence from Anhui Province, China, during the period 2011-2018

Sustainable agriculture is important for preserving environmental health and simultaneously gaining economic profits while maintaining social and economic equity. One way to evaluate sustainable agriculture is by studying agricultural eco-efficiency (AEE). Hence, this study constructed a data-driven method to evaluate and optimize AEE with the aim of providing a basis for improving the sustainable development of regional agriculture. Sixteen cities in Anhui Province, China, were considered in the study, and the variables used were agricultural resource inputs, environmental pollution, and agricultural economic development. Agricultural non-point source pollution (NPSP) emissions were considered the undesired output to build an AEE evaluation index system. Furthermore, a data envelopment analysis (DEA) model was established to analyse AEE from the static and dynamic perspectives. The spatial development and the temporal and spatial characteristics of AEE were also analysed. In addition, we applied a random effect (RE) panel Tobit model to quantitatively analyse the influencing factors of AEE from the input perspective and then proposed reasonable suggestions for improving the sustainable development of regional agriculture. Our findings show that the overall agricultural development in the 16 cities in Anhui Province has been continuously improving, even though there is an agglomeration of spatial development in some regions. In conclusion, this study provides suggestions and references for policy makers and agricultural practitioners regarding how to improve regional AEE and promote the sustainable development of the regional agricultural economy.

Assessing urban atmospheric environmental efficiency and factors influencing it in China

With rapid urbanization and industrialization in developing countries, cities have become the major sources of air pollution. Studying urban atmospheric environmental efficiency has an important reference value for the prevention and control of air pollution. This study used data from 267 cities in China between 2001 and 2016 to assess the urban atmospheric environmental efficiency using the super-efficiency slacks-based measure model, to test the spatial characteristics of urban atmospheric environmental efficiency using the spatial autocorrelation method, and to identify factors influencing it using the Geodetector. The results are as follows: (1) The atmospheric environmental efficiency of most cities in China is increasing. The average efficiency in the entire country exhibits an upward "wavy" trend. The average urban atmospheric environmental efficiency in Eastern China is the highest, and that in Western China is the lowest. (2) The urban atmospheric environmental efficiency exhibits the characteristic of global spatial autocorrelation, and high-high and low-low are the main types of efficiency in local spatial autocorrelation. (3) Population density, industrialization, and science and technology are the main factors influencing urban atmospheric environmental efficiency.

Efficiency of Implementing Climate/Energy Targets of the Europe 2020 Strategy and the Structural Diversity between Old and New Member States

The most important goals included in the Europe 2020 Strategy are climate/energy targets, which determine not only the achievement of its other goals but also climate neutrality by 2050. This article aims to assess the efficiency of implementing the climate/energy targets of the Europe 2020 Strategy and to analyse changes over time, taking into consideration the structural diversity between the old (EU 15) and new EU members (EU 13) in the period of 2014–2018. The assessment of changes in the efficiency of climate/energy targets over time adds value to the evaluation methods used to date in this area. This was done using data envelopment analysis (DEA) and the Malmquist index. Earlier works usually specified only the level of target achievement, mostly jointly in relation to all of the goals of the Europe 2020 Strategy. The efficiency of their implementation at the macroeconomic level has not been studied. Furthermore, the added value of this paper consists in obtaining additional information concerning the internal structure and character of the studied efficiency of old and new member states. Changes in the efficiency level have been analysed with regard to the key climate/energy indicators used to monitor the Europe 2020 Strategy. Based on the results, the EU countries were divided into six groups with similar levels of efficiency in achieving energy and climate objectives and ranked using the DEA–Malmquist index according to changes in their level over time. This makes it possible not only to assess the performance of countries but also to formulate recommendations for decision-makers.

New Urbanization, Energy-Intensive Industries Agglomeration and Analysis of Nitrogen Oxides Emissions Reduction Mechanisms

With the deepening of urbanization and industrialization, as well as the exacerbation of energy consumption, China is facing a severe situation in which nitrogen oxide (NOx) emissions reduction is imperative. In this study, it is aimed to put forward countermeasures and suggestions to reduce NOx emissions by analyzing the impact and mechanism of new urbanization, the agglomeration of energy-intensive industries and mutual interactions on China’s NOx emissions. By analyzing the data of 30 provinces in China from 2006 to 2017, this paper adopted the system generalized method of moments (SYS-GMM) and intermediary effect model to introduce four variables, such as: energy efficiency, human capital, industrial structure and energy structure, which were for empirical analysis. From the results, it was shown that: (1) NOx emissions in China have an accumulated effect; (2) new urbanization inhibits NOx emissions, whilst the agglomeration of energy-intensive industries intensifies NOx emissions. New urbanization weakens the negative impact of the agglomeration of energy-intensive industries on NOx emissions reduction and, (3) among the impacts of new urbanization on NOx emissions, the energy efficiency and human capital reflect the intermediary effect mechanism. At the same time, in the impact of the agglomeration of energy-intensive industries on NOx emissions, the industrial structure and energy structure show the mechanisms of the intermediary effect and masking effect, respectively.

Efficiency Evaluation of Regional Environmental Management Systems in Russia Using Data Envelopment Analysis

The concept of eco-efficiency has recently become an issue of great importance due to the growing trend of environmental degradation, and many approaches based on Data Envelopment Analysis (DEA) are used in the literature to evaluate the environmental performance of economic systems. However, research to date has paid little attention to the possibility of extending the DEA approach to the problem of measuring the economic feasibility of eco-efficiency improvement. The main aim of this study is to evaluate the efficiency of investments focused on improving the eco-efficiency of the regional economy in Russia using the DEA approach. The various types of costs for environmental protection measures are considered as inputs and the annual decrease in specific environmental impacts of the regional economy are considered as outputs of DEA models. This is different from previous research, which generally focused on environmental efficiency only, omitting the integration of economic aspects in eco-efficiency measures. This study compares three different modifications of basic DEA models in the context of technical complexity and practical feasibility. The results show that the efficiency of regional environmental management in many Russian regions has a great potential for improvement. From a practical point of view, the Slack-Based Measure (SBM) model provides the most accurate results for policy applications. Unlike other ratings, the DEA-SBM model may stimulate an optimization of environmental protection spending and the introduction of technological and organizational eco-innovations.

Environmental Efficiency Assessment of Heavy Pollution Industry by Data Envelopment Analysis and Malmquist Index Analysis: Empirical Evidence from China

Industrial waste discharged by heavy pollution industry is one of the main causes of global environmental degradation. Research on the environmental efficiency of high-polluting industry is necessary to tackle the problem of global environmental pollution. Using panel data of 19 sub-industries in China's heavy pollution industry from 2001 to 2015, this article employs Data Envelopment Analysis (DEA) and Malmquist index (MI) to measure the environmental efficiency of heavy pollution industry from both the dynamic and static perspectives. The results show that the environmental efficiency of China's heavy pollution industry maintains an upward trend but did not reach the optimal level. The general trend shows a phased trend of increasing first and then decreasing. Besides, there are inter-industry differences in the environmental efficiency across the examined sub-industries. Based on the research findings, this article proposes a set of corresponding countermeasures to solve the global pollution problem, such as reducing energy inputs and minimizing the volumes of the main categories of emissions in high-polluting industry, as well as improving the production management in the group of high environmental efficiency and strengthening technical capabilities in the group of low environmental efficiency.

Environmental Efficiency Evaluation in the Top Asian Economies: An Application of DEA

Sustainable development has become a global catchphrase in the recent development age. This leads to the growth of various methodologies in evaluating environmental efficiency, such as the Data Envelopment Analysis (DEA) method. The purpose of this study is to propose an extended DEA model, i.e., the undesirable output model, in measuring the relative eco-efficiency scores across nations. The study collected the data of inputs, namely bad outputs and good outputs of the top 20 Asian economies in the period of 2005–2019, and then estimated the environmental efficiency of each country and classified them. The results have shown that there are four nations having higher average environmental efficiency than others. Japan is a good example of sustainable development that simultaneously balances economic development and environmental protection. The study has also discussed possible solutions for improvement to the group of nations with low environmental efficiency. Contributing to applying a novelty extended DEA model, this work recommends a more precise model, taking the weight of outputs into account for further studies.

Environmental efficiency of disaggregated energy R&D expenditures in OECD: a bootstrap DEA approach

Two essential topics of Sustainable Development Goals (SDGs) are accessible which are clean energy (SDG-7) and climate change action (SDG-13). Developments and innovations in energy technologies play an essential role in achieving these goals. Therefore, any country should use energy R&D expenditures, which are the primary source of energy innovation, most optimally. This paper aims to investigate the environmental efficiency of R&D expenditures for energy efficiency, renewable energy, hydro and fuel cells, fossil energy, nuclear energy, and other power and storage technologies in OECD countries using data envelopment analysis (DEA) and bootstrap DEA. Estimation findings indicate that only the USA ensures the environmental efficiency in energy R&D expenditures among OECD countries. Although Japan, Canada, France, Germany, and Italy cannot provide environmental efficiency in energy R&D, their scores are very close to the efficiency frontier. Portugal, Hungary, and Slovak Republic are the countries with the lowest environmental efficiency in energy R&D expenditures. At the end of the investigation, this paper also provides an empirical estimation of the extent to which inefficient countries should change their R&D spending to achieve efficiency.

Chinese construction industry energy efficiency analysis with undesirable carbon emissions and construction waste outputs

As the construction industry generates more than 30% of global greenhouse gases and more than 40% of global urban waste every year, energy conservation and emission reduction has become extremely important. This study proposes an innovative output system that includes undesirable carbon dioxide and construction waste outputs. A three-stage DEA-Malmquist model is used to measure the energy efficiency of the construction industry in 30 Chinese provinces from 2008 to 2017, and a stochastic frontier method is used in the second stage to analyze and remove the energy efficiency influences of environmental factors and random errors. It was found that the total factor energy efficiency change (TFEECH) and technology change (TECH) in China's construction industry was underestimated because of the environmental factors and random errors. GRP per capita, energy consumption structures, industrial development degrees, and industrial concentrations were all found to play a positive role in improving energy efficiency; however, urbanization levels, technical equipment, policy support, and marketization were found to have a negative effect. Policy suggestions are given based on the empirical results.

Evaluating Environmentally Sustainable Development Based on the PSR Framework and Variable Weigh Analytic Hierarchy Process

Environmentally sustainable development is a multidimensional concept that emphasizes the integration of economy, society and environment within a region and the realization of dynamic balance. How to objectively environmentally sustainable development has been a major concern for scholars and policy makers. To address this problem effectively, we first obtain the indicators of environmentally sustainable development based on the pressure-state-response (PSR) framework. Then, we introduce variable weight factors in the traditional analytic hierarchy process (AHP), so that the weights assigned by experts to sustainable development indicators can change with time or space. In this way, we propose a new and improved weight distribution method called variable weigh analytic hierarchy process. Finally, we employ indicators of environmentally sustainable development based on PSR and variable weigh analytic hierarchy process to evaluate the sustainable development of cities in a case country. Our study found that: (1) indicators of environmentally sustainable development should consist of three parts: pressure indicators of environmentally sustainable development, state indicators of environmentally sustainable development, and response indicators of sustainable development; (2) with the variable weigh analytic hierarchy process, our ranking hierarchy process can handle dynamic changes among indicators better than the traditional AHP method and better reflect the true states of indicators.

An Integrated Methodology Using Extended Swara and Dea for the Performance Analysis of Wastewater Treatment Plants: Turkey Case

Public and private companies make significant water infrastructure investments to meet increasing water demand. In this context, investments in wastewater treatment plants (WWTPs), which play an important role in recycling of used water, are also increasing. This study investigates determination of the efficiency scores of WWTPs considering each metropolitan municipality as a decision-making unit (DMU). In this study, a two-step methodology is established to determine efficiency scores of WWTPs. In the first step, the input and output parameters are searched by a literature review for the performance evaluation, and candidate parameters are determined. Then, to determine the most appropriate and related parameters, the importance weights of all candidate inputs and outputs are computed using the extended stepwise weight assessment ratio analysis (SWARA) method. Next, the inputs and outputs are chosen according to their importance weights. In the second step, efficiency scores of WWTPs are calculated using output-oriented data envelopment analysis (DEA) models. Based on the expert opinions, the parameters used as input variables are as follows: Daily Wastewater Amount per Person Discharged in Municipalities, WWTP Capacity, and Number of WWTPs; and the parameters used as output variables are as follows; Amount of Wastewater Treated in WWTPs and Municipal Population Served by WWTPs. The results are presented and discussed by sensitivity analysis. Results show that 14 metropolitan municipalities have total efficiency, 19 metropolitan municipalities have technical efficiency, and 21 metropolitan municipalities have scale efficiency.

Illuminating the efficiency of CO2 emissions in China's mining sector: evidence from meta-frontier Malmquist index models

CO₂ emission reduction has become a binding target in China's economic and social development. The mining industry is China's primary energy consumer and therefore has an important influence on China's CO₂ emissions. Herein, we conduct a comprehensive analysis of data from the Chinese mining industry between 2004 and 2015. And we employ meta-frontier data envelopment analysis to analyse the CO₂ emission efficiency of the Chinese mining industry and identify the driving factors that influence the observed dynamic changes in CO₂ emission efficiency. The main practical conclusions of our research are as follows: (1) the CO₂ emission efficiency of China's mining industry had grown continuously during the study period; (2) during the study period, the CO₂ emission efficiency of China's mining industry did not change consistently with changes in different geographical regions; (3) the overall efficiency of management in China's mining sector was low; (4) the small and medium enterprises (SMEs) in China's mining sector played a significant role in the improvement of the CO₂ emission efficiency. The empirical results provide constructive policy implications that should be considered by the policy makers for the more sustainable development of China's mining sector.

An integrated data envelopment analysis and life cycle assessment method for performance measurement in green construction management

The construction industry routinely extracts vast quantities of materials and releases deleterious pollutant emissions to the biosphere. In this study, we propose an integrated data envelopment analysis (DEA) and life cycle assessment (LCA) method to measure the performance of eco-friendly building materials in green construction management. Initially, we use the LCA method and environmental impact analysis to identify alternative green flooring systems and relevant sustainability criteria. We then use factor analysis to further evaluate these criteria and choose the most significant sustainability factors. Finally, a DEA model and a new enhanced Russell model (ERM) is proposed to measure the performance of the green flooring systems with factor analysis.

Sustainability and Energy Efficiency: BIM 6D. Study of the BIM Methodology Applied to Hospital Buildings. Value of Interior Lighting and Daylight in Energy Simulation

One of the least used aspects of BIM (Building Information Modeling) is the ability to obtain the energy model of the building using the BIM methodology known as BIM 6D. This digital information model allows simulating the real energy behavior of the building and the improvement in the building’s lighting systems, both natural and artificial, in particular daylighting. In this way, the BIM 6D simulation allows us to make design and operation decisions for the building, not only for new buildings that must be, in accordance with current legislation, NZEB (Nearly Zero-Energy Building) but also for the rehabilitation of existing buildings. Particularly in buildings for sanitary use, BIM 6D allows an exhaustive analysis of the energy impact of said rehabilitation, guiding it towards an improvement in energy and light efficiency, which in turn provides greater quality and comfort in the use of the sustainable building. This subject of study is especially important in public buildings for hospital use. Buildings where energy efficiency and comfort, oriented towards optimal and efficient lighting, are two fundamental criteria highly appreciated by patients and citizens in general. Once the energy model of the building has been obtained, it is possible to study and identify possible alternatives to improve energy efficiency and improve lighting, as well as to analyze the possibilities of incorporating other more efficient forms of renewable energy, such as the use of daylight. In this work we can see how applying a set of simulated improvement actions in BIM 6D achieves an energy saving of 50% in general and up to 13% only by acting on lighting systems, allowing the decarbonization of buildings with high energy consumption, such as hospitals, and in turn, will lead to an improvement in the energy certification of these buildings; thus achieving a better and higher quality of habitability, using more efficient forms of lighting and transforming buildings into more sustainable spaces.

Thermochemical and Economic Analysis for Energy Recovery by the Gasification of WEEE Plastic Waste from the Disassembly of Large-Scale Outdoor Obsolete Luminaires by LEDs in the Alto Alentejo Region (Portugal)

The recovery of urban waste is a social demand and a measure of the energy-environmental sustainability of cities and regions. In particular, waste of electrical origin, waste of electrical and electronic materials (WEEE) can be recovered with great success. The plastic fraction of these wastes allows their gasification mixed with biomass, and the results allow for producing syngas with a higher energy potential. This work allows for obtaining energy from the recovery of obsolete materials through thermochemical conversion processes of the plastic waste from the disassembly of the luminaires by mixing the said plastic waste in different proportions with the biomass of crop residues (olive). The gasification tests of these mixtures were carried out in a downstream fixed-bed drown daft reactor, at temperatures of approximately 800 °C. The results demonstrate the applied technical and economic feasibility of the technology by thermal gasification, for the production of LHV (Low Heating Value) syngas with highest power energy (more than 5 MJ/m3) produced in mixtures of up to 20% of plastic waste. This study was complemented with the economic-financial analysis. This research can be used as a case study for the energy recovery through gasification processes of plastic waste from luminaires (WEEE), mixed with agricultural biomass that is planned to be carried out on a large scale in the Alentejo (Portugal), as a solution applied in circular economy strategies.

Analysis of CO2 Drivers and Emissions Forecast in a Typical Industry-Oriented County: Changxing County, China

Decomposing main drivers of CO2 emissions and predicting the trend of it are the key to promoting low-carbon development for coping with climate change based on controlling GHG emissions. Here, we decomposed six drivers of CO2 emissions in Changxing County using the Logarithmic Mean Divisia Index (LMDI) method. We then constructed a model for CO2 emissions prediction based on a revised version of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and used it to simulate energy-related CO2 emissions in five scenarios. Results show that: (1) From 2010 to 2017, the economic output effect was a significant, direct, dominant, and long-term driver of increasing CO2 emissions; (2) The STIRPAT model predicted that energy structure will be the decisive factor restricting total CO2 emissions from 2018 to 2035; (3) Low-carbon development in the electric power sector is the best strategy for Changxing to achieve low-carbon development. Under the tested scenarios, Changxing will likely reach peak total CO2 emissions (17.95 million tons) by 2030. Measures focused on optimizing the overall industrial structure, adjusting the internal industry sector, and optimizing the energy structure can help industry-oriented counties achieve targeted carbon reduction and control, while simultaneously achieving rapid economic development.