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Paper and pulp manufacturing industry produces versatile products from renewable feedstock that are easily recycled. It is the fourth largest industrial sector in terms of energy use. Much of the energy used comes from biomass derived fuels or high efficiency combined heat and power plants so the industry is not considered as carbon intensive. But at production paper making emits five times the CO2/tonne of steel; this is gradually removed from the atmosphere by the growth of replacement trees which can take between 7 and 90 years. This study reviewed existing literature to establish estimates for future energy requirements, and way that these could be met with minimum carbon emissions in a world where there are electricity grids with low carbon intensities, high recycling rates and growing demand for sustainable biomass. It was found that energy consumption could be reduced by 20% using technologies that have been demonstrated at an industrial scale. Most virgin pulp is made using the kraft chemical processing method. It was found that it should be possible to eliminate all fossil fuel use from this process, by combustion of by-product while exporting a small amount of electricity. Recycled paper is becoming the largest source of pulp. In this case the waste streams cannot provide sufficient energy to power the process, but process heat can be produced by burning some of the collected waste paper in steam plants or by using electric heat pumps. The energy needed to produce high quality office paper is nearly twice that required for non-deinked packaging paper. This couples with the lower pulp yields obtained with high quality pulp means that the environmentally preferred option for energy supply to the recycling process is dependent on the grade of pulp being produced.

This study examines the deteriorating inventory management problem for items with short shelf life, considering alternative carbon control strategies from 2018 to 2023. These policies include carbon cap and trade, trade and credit policy, carbon-emission control, and others. The study takes into account critical elements such as shelf life, optimal policies, modelling approaches, greening effects, subsidies, and rework policies. The analysis started with a search for 'EOQ Model' in the Science Direct database, which generated 788 items. For a comprehensive evaluation, were restricted our resources to 329 scientific publications, including deterioration. Following that, it was limited to carbon emissions, obtaining 123 results. The papers referenced above cover a wide range of issues, including remanufacturing and rework, as well as carbon caps and trade-credit systems for data collection, yielding 45 and 32 research articles, respectively. The review prioritizes respected publications of peer-reviewed journals papers for reliable results were examined. A review of the literature suggested that future research should concentrate on stochastic modelling. The emphasis has been placed on identifying future study gaps that will aid in the development of most relevant models. The current work will serve as a guideline for selecting the suitable mathematical technique(s) and methodology(s) in various situations involving deteriorating items. The current analysis examined 42 research papers on deteriorating inventory modelling accessible in the literature to characterize its current state and indicate probable future directions. Future research needs have also been identified. This comprehensive study is firmly believed to fill a knowledge gap on deteriorating inventory and support in the formulation of appropriate methods for the creation of a successful and effective inventory control system for deteriorating products.

Abstract This paper adopts the “baseline scenario method” to construct a comprehensive model for calculating and reducing carbon emissions generated by employee travel, including the accounting of carbon emissions from commuting and business travel, as well as the assessment of green travel for carbon reduction. The study employs methods such as questionnaires and on-site interviews to collect travel data from employees of a research institute in Beijing as a case study. The results show that employees’ commuting methods are diverse, with the subway being the primary mode of travel; however, business travel generates higher carbon emissions, particularly among employees with higher education levels. The research concludes that the model proposed in this paper provides a framework for preliminary carbon emission estimation, but to improve the accuracy of the estimates, more variables and factors need to be considered, and the limitations of the model are pointed out. The research findings have significant implications for policy and institutional practices, suggesting the adoption of more targeted measures to reduce the use of high-carbon-emission travel methods and to encourage the use of green travel options. With the continuous advancement of data collection technologies in the future, it will be possible to further establish a more refined carbon emission accounting model and obtain more accurate and comprehensive travel data, thereby providing solid data support for the development of more effective carbon reduction strategies and policies.

Abstract Background The Association of Southeast Asian Nations (ASEAN) relies mainly on fossil fuels in their energy supply, leading to higher CO2 emissions, pollution, and further environmental degradation. This paper uses the panel vector autoregressive and the Granger non-causality test in the heterogeneous panels, together with long-run estimation techniques, to examine the dynamic link among energy consumption, economic growth, and carbon emissions with the focus on renewable energy for the ASEAN countries in the past three decades. Results The findings from this paper indicate that carbon emissions are associated with energy consumption. In contrast, renewable energy usage reduces CO2 emissions, improving environmental quality. Economic growth is associated with increased energy consumption and carbon emissions in the ASEAN countries. The findings also indicate that the effects of energy consumption on economic growth are more significant than those of renewable energy in ASEAN. When considered together, these findings form a vicious circle regarding the energy–growth–emission nexus for the ASEAN economies. In addition, a bidirectional Granger causality among energy consumption, economic growth, CO2 emissions and renewable energy usage is confirmed. Conclusions Renewable energy has emerged as an important viable option for the ASEAN nations to achieve their dual objectives of enhanced economic growth, reduced CO2 emission, leading to improved environmental quality.

Cities are the core carriers and key positions to achieve the dual carbon goals. It is of great significance to explore whether promoting urbanization can improve carbon emission performance, which is of great significance to comprehensively promote the goal of carbon neutrality. Based on the panel data from 2006 to 2021, this paper analyzes the spatial autocorrelation of carbon emission performance per unit space and the impact mechanism of urbanization process on it. The fixed-effect model was further used to identify the influencing factors of spatial carbon emission performance. The results show that: 1) China’s carbon emission performance per unit space is declining year by year. 2) There is a strong positive spatial correlation and stable path dependence on the performance of carbon emissions per unit space in each region. 3) To a certain extent, increasing the level of urbanization will reduce the carbon emission performance per unit space. 4) The urbanization process has a spatial spillover effect on the carbon emission performance per unit space of surrounding provinces, and the spatial spillover effect of industrial structure and energy consumption structure is more obvious than that of economic level, population density and urbanization rate. Based on the conclusions, this paper puts forward specific policy suggestions to reduce the carbon emission performance per unit space to help the low-carbon development of cities.

To effectively address climate change, it is necessary to quantify the carbon emissions in high energy-consuming regions, analyze driving factors, and explore effective pathways for achieving green development. Therefore, this paper takes Liaoning Province as research area, using extended Kaya identity and LMDI method to analyze the driving factors of carbon emissions from energy consumption in five major industries and the residential consumption sector from 2011 to 2020 in Liaoning Province. Furthermore, this paper uses the Tapio model to explore the decoupling relationship between carbon emissions and economic development. The results show that: 1) From 2011 to 2020, total carbon emissions from energy consumption in five major industries showed a trend of initially declining and then rising, while carbon emissions from the residential consumption sector exhibited an upward trend. 2) For carbon emissions from the industrial sector, economic output and industrial structure are the primary factors that promote and inhibit carbon emissions respectively. The inhibitory effects of energy structure and energy intensity are not significant. Population scale has a certain promoting effect on carbon emissions. For residential energy consumption carbon emissions, Household consumption expenditure, residential energy structure, and residential population scale are driving factors that promote the growth of carbon emissions, while residential energy intensity restrains the growth of carbon emissions. 3) From 2011 to 2018, carbon emissions from the industrial sector have been decoupled from economic output, and the decoupling state is dominated by weak decoupling. However, carbon emissions are once again correlated with economic development in 2019–2020. Carbon emissions from residential energy consumption have not yet decoupled from consumption expenditure, and its decoupling state is unstable and has no obvious change rule.

This paper studies the impact of digital economy development on carbon emissions, which is a hot topic in the field of environmental economics. This topic is within the scope of the Frontiers in Environmental Science and is particularly in line with the theme of Environmental Economics and Management section in the journal. Firstly, this paper constructs the Digital Economy Development Index and its variable indicators to measure the development of the digital economy. Secondly, this paper uses urban panel data to reveal the impact of digital economy development on carbon emissions, and further adopts IV method to deal with endogeneity issues. Thirdly, this paper performs the heterogeneity analyses depending on the characteristics of the city. The results can provide a basis for formulating differentiated policies. Fourthly, this paper further explores the mechanism by which the digital economy affects carbon peaking. The results show that the digital economy may influence carbon peaking through upgrading of the industrial structure, technical innovation, and energy consumption.

This paper analyzes the theoretical mechanism by which urbanization and consumption structure upgrading exert an impact on carbon emissions in the context of promoting higher quality and efficiency in urbanization, advancing the transition and upgrading of consumption structure, and achieving carbon peaking and carbon neutrality (“dual carbon”) goals. By establishing an extended STIRPAT model and an intermediary model, this paper takes nine prefecture-level cities in Fujian Province from 2011 to 2021 as samples to investigate the effects of urbanization and consumption structure upgrading on carbon emission reduction in Fujian Province, revealing the interrelationship and transmission mechanism among the three. The results of this study show that: (i) The relationship between urbanization and carbon emissions in Fujian Province from 2011 to 2021 shows an inverted U-shaped curve, i.e. urbanization accelerated carbon emissions in the early stage and inhibited carbon emissions in the later stage; (ii) the regional heterogeneity analysis shows that under the current urbanization background, the upgrading of consumption structure in western and southern Fujian can significantly reduce carbon emissions, while the relationship between consumption structure upgrading and carbon emission reduction in eastern Fujian is not significant; (iii) the upgrading of consumption structure can help achieve and promote carbon emission reduction through increasing the proportions of the tertiary industry and immaterial consumption and enhancing the level of culture and education, while exerting little effect on carbon emission reduction through consumption in transportation, communication and health care.

Sustainability continues to be a key field of study, encapsulating three principle dimensions: social, economic and environmental, which are also found within the context of climate change. However, there appears to be limited literature drawing upon the between sustainability and climate change. relationship, particularly in connection to carbon emissions and energy management. These issues have already been the subject of legislation in different countries, though still, predominantly individually rather than from an integrated perspective. The Climate Change At (2008) provides a platform through which the relationship between sustainability and climate change can be considered. This paper establishes this relationship aspects of this relationship by employing the increased use of insulation within the UK housing stock to contribute to achieving the carbon reduction set by the Act. Taking a retrospective view through theoretical numerical modelling, this paper demonstrates that CO2 reductions were achievable. The results demonstrate that links can be drawn between sustainability and climate change and identifies that significant CO2 savings, through robust energy management of the UK housing stock, these results can be achieved. It is also suggested that the theoretical mole developed can be reproduced to consider climate change targets and provide benchmarks, not only in the UK but in other countries.

The primary cause of high carbon emissions due to inefficient transportation is the lack of cooperation among various logistics companies during distribution. Joint distribution mode represents an effective means to address this issue. However, research on the joint distribution mode primarily focuses on the centralized joint distribution mode. In the context of the decentralized joint distribution mode, logistics companies can autonomously choose their distribution routes based on the orders they are dispatched. If logistics companies unilaterally aim to reduce their own costs, it can easily lead to an increase in carbon emissions from the alliance, which can have adverse effects. This paper focuses on the decentralized joint distribution alliance and considers reducing carbon emissions through the allocation of the alliance’s order. A two-stage approach is proposed, based on routing optimization and worst-case estimation. The mathematical models for each stage are established and algorithms are designed. Subsequently, management recommendations are provided through numerical analysis. This paper aims to explore methods to address the challenges faced by decentralized joint distribution alliances.

Purpose – There has been an increase in research examining whether and how companies disclose climate change impacts and how these disclosures influence capital structure strategies in recent years. However, prior literature has generally focused on developed countries. This paper proposes to examine the impact of voluntary climate change disclosures on corporate financing decisions in an emerging economy. Design/methodology/approach – The dataset includes 335 firm-year observations listed in the Borsa Istanbul (BIST) 100 manufacturing industry firms that participated in the Carbon Disclosure Project (CDP) questionnaire from 2016 to 2020, characterized by high public awareness of greenhouse gas emissions in Turkey. To accomplish this aim, two models have been constructed that link capital structure strategies with voluntary corporate climate change disclosures while controlling for firm-level attributes in terms of size, profitability, market value and free float ratio (FFR). Findings – The significant and negative relationship between the voluntary disclosure of climate-related activities and long-term borrowing is consistent with the arguments that companies with high commitments are unlikely to reduce default risk in emerging markets. This paper also provides empirical evidence that the high size and the level of low profitability magnify this relationship between CDP and financial leverage. Originality/value – The Paris Agreement seems to be a significant point where corporate lenders have become aware of the commitment of policymakers to fight climate change. The results have significant implications for both managerial strategies and environmental regulatory policy-making issues. In addition, the findings shed light on the strategic behavior of managers in the consideration of climate change risks and related transparency.

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation.

The Logarithmic Mean Divisia Index (LMDI) method is widely applied in research on carbon emissions, urban energy consumption, and the building sector, and is useful for theoretical research and evaluation. The approach is especially beneficial for combating climate change and encouraging energy transitions. During the method’s development, there are opportunities to develop advanced formulas to improve the accuracy of studies, as indicated by past research, that have yet to be fully explored through experimentation. This study reviews previous research on the LMDI method in the context of building carbon emissions, offering a comprehensive overview of its application. It summarizes the technical foundations, applications, and evaluations of the LMDI method and analyzes the major research trends and common calculation methods used in the past 25 years in the LMDI-related field. Moreover, it reviews the use of the LMDI in the building sector, urban energy, and carbon emissions and discusses other methods, such as the Generalized Divisia Index Method (GDIM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) techniques. This study explores and compares the advantages and disadvantages of these methods and their use in the building sector to the LMDI. Finally, this paper concludes by highlighting future possibilities of the LMDI, suggesting how the LMDI can be integrated with other models for more comprehensive analysis. However, in current research, there is still a lack of an extensive study of the driving factors in low-carbon city development. The previous related studies often focused on single factors or specific domains without an interdisciplinary understanding of the interactions between factors. Moreover, traditional decomposition methods, such as the LMDI, face challenges in handling large-scale data and highly depend on data quality. Together with the estimation of kernel density and spatial correlation analysis, the enhanced LMDI method overcomes these drawbacks by offering a more comprehensive review of the drivers of energy usage and carbon emissions. Integrating machine learning and big data technologies can enhance data-processing capabilities and analytical accuracy, offering scientific policy recommendations and practical tools for low-carbon city development. Through particular case studies, this paper indicates the effectiveness of these approaches and proposes measures that include optimizing building design, enhancing energy efficiency, and refining energy-management procedures. These efforts aim to promote smart cities and achieve sustainable development goals.

As the EU strives to achieve its climate goals, it is becoming increasingly crucial to understand the complex relationships between economic activity, energy consumption, and carbon emissions. In this context, our paper aims to investigate the correlation between carbon emissions, energy consumption, and economic development. To fulfill our aim, we have used Eurostat and OECD data for the EU-27 member states for a period of 13 years (2010–2022), using a linear regression as the main analysis method. Our results have shown that there is a strong correlation between demand-based and production-based CO2 emissions as well as between production-based CO2 emissions and final energy consumption, while at the same time, our findings have shown that there is no direct correlation between energy consumption and economic development, aligning our study with the neutrality hypothesis of the energy growth nexus. This paper expands the ongoing discussion on sustainable development and climate change mitigation by conducting a thorough analysis of the EU-27 countries over a span of thirteen years. The results emphasize the need for integrated strategies that address both production and consumption emissions, emphasize the vital role of energy efficiency, and raise questions about the effectiveness of increasing energy consumption to enhance economic productivity or CO2 efficiency.

Abstract As the world’s largest energy consumer and carbon emitter, the task of carbon emission reduction is imminent. In order to realize the dual-carbon goal at an early date, it is necessary to study the key factors affecting China’s carbon emissions and their non-linear relationships. This paper compares the performance of six machine learning algorithms to that of traditional econometric models in predicting carbon emissions in China from 2011 to 2020 using panel data from 254 cities in China. Specifically, it analyzes the comparative importance of domestic economic, external economic, and policy uncertainty factors as well as the nonparametric relationship between these factors and carbon emissions based on the Extra-trees model. Results show that energy consumption (ENC) remains the root cause of increased carbon emissions among domestic economic factors, although government intervention (GOV) and digital finance (DIG) can significantly reduce it. Next, among the external economic and policy uncertainty factors, foreign direct investment (FDI) and economic policy uncertainty (EPU) are important factors influencing carbon emissions, and the partial dependence plots (PDPs) confirm the pollution haven hypothesis and also reveal the role of EPU in reducing carbon emissions. The heterogeneity of factors affecting carbon emissions is also analyzed under different city sizes, and it is found that ENC is a common driving factor in cities of different sizes, but there are some differences. Finally, appropriate policy recommendations are proposed by us to help China move rapidly towards a green and sustainable development path.

Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China’s Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future.

In this paper, panel data from Chinese cities from 2000 to 2020 were used, and the intellectual property pilot was used as a quasi-natural experiment. The double difference model mainly tests the underlying mechanism of the impact of intellectual property rights pilots on urban carbon emissions. This study revealed that IPR pilots play an important role in reducing urban carbon emissions. Heterogeneity experiments reveal that IPR pilots are conducive to reducing carbon emissions in coastal as well as high greening cities and that the underlying mechanism through which IPR pilots affect urban carbon emissions mainly occurs at the level of innovation and the optimization of the industrial structure. In addition, this paper also validates the smoothness of the model by replacing the explanatory variables and performing a placebo test, which further strengthens the reliability of the paper’s conclusions.

The majority of anthropogenic carbon dioxide emissions originate from cities, nonetheless, it is common to have inaccuracies and uncertainties in urban carbon emission measurement. Many countries have devoted much effort to improve the accuracy and consistency of urban emission data through improving measurement tools and inversion models. The paper presents achievements of research in the U.S and Europe, as well as discusses their technical impacts and obstacles. The paper provides an overwiew of the technical impacts and research gaps of CO2-USA network and the Carbosense CO2 sensor network in Switzerland, we discuss the spatial and temporal distribution characteristics of greenhouse gas concentrations and explain the principles of carbon emission inversion method. The CO2-USA network applies an interdisciplinary approach to estimate urban carbon emission to simulate the transport process of CO2 molecules in the atmosphere. The Carbosense network employs dense networks to observe the changing process of carbon dioxide emissions via enhancing data quality of low-cost carbon dioxide sensors. The purpose of this paper is to propose a novel framework of GHG observing network which combines carbon emission estimation method and real-time greenhouse gas monitoring network. The framework applies in-situ sites and mobile observations to build high-resolution emissions inventories and gain greenhouse gas concentration mappings in the urban regions. It provides governments with reliable measurement results for planning research directions for carbon emission management in China over the next few years.

[Objective] To improve the carbon quota allocation method based on historical carbon emissions and advance the development of the carbon trading market in China, a two-level and double quota distribution system for carbon quota allocation was developed. [Methods] This study used Markov chain to divide 32 industrial sectors into three clubs (high, medium, and low-carbon clubs). It used carbon emissions and carbon intensity as indicators for primary and secondary allocation, respectively. The Dagum Gini decomposition method was used to measure the carbon emission differences of the clubs or sectors and determine the correction coefficients of the level of difference and the base-period of allocation. [Results] (1) The high, medium, and low-carbon clubs consisted of 6, 6, and 12 members respectively and their differentiation are remarkable. The high-carbon club included coal mining and washing, chemical raw material and manufacturing, non-metallic minerals manufacturing, ferrous metal smelting and rolling processing, non-ferrous metal smelting and rolling processing, and electricity and heat producing and supplying industries. The medium-carbon club included ferrous metal mining and dressing, non-ferrous metal mining and dressing, non-metal mining and dressing, agricultural and subsidiary foodstuff processing, paper making and paper product, and petroleum refining, coking, and nuclear fuel processing industries. (2) 2018 was the inflection point of differentiation, and using 2018 to 2020 as the base-period was more equitable. (3) Within the base-period, the change of carbon emission intensity varied greatly between clubs or sectors, and implementing secondary allocation first for the punished clubs, the punished high-carbon industries, and the rewarded medium-carbon industries helped to establish a clear reward and punishment system. (4) The ranges of primary allocation correction for the clubs and the high and medium-carbon industries were reasonable, which were from -0.36% to 2.41%, -4.41% to 9.60%, and -8.14% to 2.78%, respectively. (5) Compared to the one-level and one-time allocation system, this proposed system was better in stimulating industries to lower carbon emission intensity, and the carbon quota had been adjusted between -8.14% and 9.60% from the original system. [Conclusion] It is scientific and sound to use Markov chain to establish a two-level allocation system and use the Gini coefficient to determine the base-period and coefficients of the level of difference. The initial quota of the industries under this system not only can fully reflect the carbon emission level of each industry in the base-period, but also can reflect the magnitude of change of carbon emission intensity of the industries. Taking the trend of change of carbon emission differences as the theoretical basis for the selection of the base-period can provide a practical guidance for carbon quota allocation.

The country’s low-carbon policy makes the new energy industry vigorously promoted, and at the same time, the addition of artificial intelligence technology also makes the new energy and traditional fuel vehicles pull apart. This paper decomposes the influencing factors of transportation carbon emission by measuring the energy saving and emission reduction of transportation of new energy vehicles, using the extended Kaya constant equation of transportation carbon emission and the generalized Fisher index method. From the government subsidies and tax incentives in low carbon policies, the impact of policies on the performance of new energy vehicle enterprises is explored by analyzing the empirical regression results. The results show that the correlation coefficients of financial subsidies and enterprise performance are 0.135, 0.09, and 0.081, and the correlation coefficients of tax preferences and enterprise performance are 0.075, 0.172, 0.08, respectively, which indicate that low-carbon policies have positive incentive effects on new energy vehicle marketing. This paper provides a reference for the related low-carbon policy incentive theory.

Facing the dual pressure of domestic environmental pollution and international climate change mitigation, China, the world’s largest emitter of carbon emissions, has proposed a “double carbon” target of 3060. Fujian Province is a clean energy province and a major economic province in southeastern China, with inherent energy conservation and emission reduction advantages. However, as industrialization is still in progress and urbanization is accelerating, the problems of low industrial level and sloppy development are still prominent, and the future low-carbon transformation will be difficult and long. Based on the theory of industrial structure optimization and the theory of low carbon economy, this paper analyzes the influence mechanism of industrial structure optimization on carbon emission from three perspectives: efficiency improvement, technological progress, and structural adjustment. Secondly, an econometric model is constructed by combining the environmental Kuznets curve, and the time series data of Fujian Province from 2000-2021 are analyzed empirically to derive the optimization path of industrial structure on carbon emissions in Fujian Province through co-integration analysis, multiple regression, mediating effect analysis, and robustness testing methods. The results of this paper show that from the structure of energy consumption in Fujian Province, coal and oil consumption account for 72% of the total consumption, which is the main cause of carbon emissions in Fujian Province. The mediating effect and stability analysis of the optimization path through the benchmark regression model shows that the p-value variance is 0.0862, and there is no significant difference between them. The optimization path proposed in this paper has significant utility for carbon emission reduction, improves the efficiency of energy use, strengthens the introduction of FDI and industrial structure optimization in various regions of Fujian Province in a targeted manner, and provides a reference for decision-making for global industrial structure optimization and upgrading.

Climate change has become a global nightmare, and the awareness of the causes of carbon emissions has resulted in rigorous studies. These studies linked the increase in global warming with booming economic growth. Since global warming has become more apparent, researchers have explored ways to decouple economic activities from carbon growth. Economic and carbon growth must be decoupled to achieve a low-carbon economy to support the carbon-growth plan or emission-reduction strategy. The world is transitioning toward a carbon-neutral and green ecosystem, so finding ways to decouple carbon emissions from economic activities is an exciting topic to explore. This study reviews current information on the importance of decoupling energy from economic growth innovative techniques that thoroughly examine the challenges and constraints of low-carbon energy systems. In order to examine the detrimental effects of carbon emissions on ecosystems and the ways in which economic expansion contributes to carbon footprints, more than three hundred research papers were gathered using several search engines, including Elsevier and Google Scholar. This review revealed that decarbonization and dematerialization had been achieved without declining global economic growth. It also provides information on energy use and economic activities leading to global carbon emissions and alternative solutions to the global challenge of climate change. The decoupling methods commonly used to determine the impact of energy decarbonization on economic growth are explored. All the results suggest that economic growth is a primary mover of global carbon emission increase and must be separated to achieve a carbon environment.

Purpose – The purpose of this paper is to asses the welfare and macroeconomic implications of three distinct degrowth strategies designed to reduce carbon emissions: penalizing fossil fuel demand, substituting aggregate consumption with leisure and disincentivizing total factor productivity (TFP) growth. Design/methodology/approach – Using an environmental dynamic general equilibrium (eDGE) model that incorporates both green renewable technologies and fossil fuels in the production process, this study sets an emissions reduction target aligned with the goals of the Paris Agreement by 2050. Findings – The results reveal that the conventional degrowth strategy, wherein a reduction in the consumption of goods and services is compensated with an increase in leisure, may entail significant economic consequences, leading to a notable decline in welfare. In particular, a degrowth scenario resulting from a decline in TFP yields the most pronounced reduction in welfare. Conversely, inducing a reduction in fossil fuel demand by fiscally inflating the price of the imported commodity, despite potential social backlash, exhibits noticeably less detrimental welfare effects compared to other degrowth policies. Furthermore, under this degrowth strategy, the findings suggest that a globally coordinated strategy could result in long-term welfare gain. Originality/value – To the best of the authors’ knowledge, this is the first contribution that uses an eDGE model to evaluate the welfare implications of an additional degrowth strategy amidst the ongoing inertial reduction of carbon emissions.

ObjectiveThe dynamic prediction of carbon emission from cold chain distribution is an important basis for the accurate assessment of carbon emission and its green credit grade. Facing the fact that the carbon emission of vehicles is affected by multiple factors, such as road condition information, driving characteristics, refrigeration parameters, etc., a dynamic prediction model of carbon emission was proposed from refrigerated vehicles that integrates multi-source information.MethodsThe backbone feature extraction network, neck feature fusion network and loss function of YOLOv8s was firstly improved. The full-dimensional dynamic convolution was introduced into the backbone feature extraction network, and the multidimensional attention mechanism was introduced to capture the contextual key information to improve the model feature extraction capability. A progressive feature pyramid network was introduced into the feature extraction network, which reduced the loss of key information by fusing features layer by layer and improved the feature fusion efficiency. The road condition information recognition model based on improved YOLOv8s was constructed to characterize the road condition information in terms of the number of road vehicles and the percentage of pixel area. Pearson's correlation coefficient was used to compare and analyze the correlation between carbon emissions of refrigerated vehicles and different influencing factors, and to verify the necessity and criticality of the selection of input parameters of the carbon emission prediction model. Then the iTransformer temporal prediction model was improved, and the external attention mechanism was introduced to enhance the feature extraction ability of iTransformer model and reduce the computational complexity. The dynamic prediction model of carbon emission of refrigerated vehicles based on the improved iTransformer was constructed by taking the road condition information, driving characteristics (speed, acceleration), cargo weight, and refrigeration parameters (temperature, power) as inputs. Finally, the model was compared and analyzed with other models to verify the robustness of the road condition information and the prediction accuracy of the vehicle carbon emission dynamic prediction model, respectively.Results and DiscussionsThe results of correlation analysis showed that the vehicle driving parameters were the main factor affecting the intensity of vehicle carbon emissions, with a correlation of 0.841. The second factor was cargo weight, with a correlation of 0.807, which had a strong positive correlation. Compared with the vehicle refrigeration parameters, the road condition information had a stronger correlation between vehicle carbon emissions, the correlation between refrigeration parameters and the vehicle carbon emissions impact factor were above 0.67. In order to further ensure the accuracy of the vehicle carbon emissions prediction model, The paper was selected as the input parameters for the carbon emissions prediction model. The improved YOLOv8s road information recognition model achieved 98.1%, 95.5%, and 98.4% in precision, recall, and average recognition accuracy, which were 1.2%, 3.7%, and 0.2% higher than YOLOv8s, respectively, with the number of parameters and the amount of computation being reduced by 12.5% and 31.4%, and the speed of detection being increased by 5.4%. This was due to the cross-dimensional feature learning through full-dimensional dynamic convolution, which fully captured the key information and improved the feature extraction capability of the model, and through the progressive feature pyramid network after fusing the information between different classes through gradual step-by-step fusion, which fully retained the important feature information and improved the recognition accuracy of the model. The predictive performance of the improved iTransformer carbon emission prediction model was better than other time series prediction models, and its prediction curve was closest to the real carbon emission curve with the best fitting effect. The introduction of the external attention mechanism significantly improved the prediction accuracy, and its MSE, MAE, RMSE and R2 were 0.026 1 %VOL, 0.079 1 %VOL, 0.161 5 %VOL and 0.940 0, respectively, which were 0.4%, 15.3%, 8.7% and 1.3% lower, respectively, when compared with iTransformer. As the degree of road congestion increased, the prediction accuracy of the constructed carbon emission prediction model increased.ConclusionsThe carbon emission prediction model for cold chain distribution under multi-source information fusion proposed in this study can realize accurate prediction of carbon emission from refrigerated vehicles, provide theoretical basis for rationally formulating carbon emission reduction strategies and promoting the development of low-carbon cold chain distribution.

[Objective] In the context of increasingly intensifying conflicts between resources and environment and economic growth, the purpose of this paper is to explore how the “innovation-finance” policy portfolio affects urban carbon emissions from the perspective of policy instrument portfolio, so as to provide policy suggestions for achieving the “dual carbon” goal and building an efficient policy coordination path. [Methods] Based on the panel data of 273 cities at the prefecture level and above in China from 2006 to 2020, this study used the “dual pilot” of innovative cities and the pilot of combining science and technology with finance to construct quasi-natural experiments, and used the multi-phase difference-in-differences (DID) model to investigate the synergistic carbon emission reduction effect of “innovation-finance” policy instrument portfolio. [Results] (1) The synergy of the “dual pilot” policy was significantly conducive to carbon emission reduction and it can exert a more obvious carbon emission reduction effect than the “single pilot” policy, and the above conclusion was still true after a series of robustness tests; (2) The “dual pilot” policy indirectly reduce carbon emissions by driving green technology innovation and accelerating talent agglomeration; (3) The synergistic carbon emission reduction effect of the “dual pilot” policy was more obvious in non-resource-based cities, cities at higher administrative levels, and cities with shorter tenured officials, and there was a more obvious synergistic carbon emission reduction effect in the scenario of promoting the integration of science and technology and finance followed by carrying out innovative city construction. [Conclusion] It is necessary to give full play to the effect of the “innovation-finance” policy portfolio, accelerate the release of carbon emission reduction dividends from policy coordination, and explore an effective path of policy coordination to achieve the dual carbon goals.

This article provides a cause-and-effect analysis of the impact of carbon emissions on climate change and on the sustainable development of European countries. Despite the consolidated efforts of the world community, the global amount of greenhouse gas emissions is not decreasing, leading to irreversible climate change. The objective of this paper is to explore Europe’s progress towards climate neutrality in the context of ensuring sustainable development and achieving the goals of the European Green Deal. The article aims to establish an econometric relationship between the amount of carbon emissions and the energy intensity of GDP based on statistical data from European countries, and using the example of Ukraine during a full-scale invasion to demonstrate factors that influence greenhouse-gas- emissions growth despite GDP. A significant aspect of this study is an analysis of data from 2012–2022, which indicates that Europe has reduced CO2 emissions and is successfully moving towards climate neutrality. Key efforts of EU countries in preventing climate change and transitioning to renewable energy sources are reflected in the context of the Green Deal. Based on econometric calculations, the direct relationship between the amount of carbon emissions and energy intensity of GDP in European countries was confirmed with a probability of 0.95. The obtained interdependence allows one to predict the amount of CO2 emissions based on known values of energy intensity of GDP during stable economic development. Special attention was paid to Ukraine and the increase in CO2 emissions due to the war. The authors concluded that the war disrupted the relationship between GDP and CO2 emissions, leading to a 23% increase in emissions in 2022. Overcoming the climate crisis and ensuring sustainable development requires a global decarbonisation strategy. However, effective climate policy is impossible without achieving peace.

Abstract The pursuit of economic growth has implications for carbon emissions and climate change. Achieving low carbon development is important for attaining the targets of the sustainable development goals. Africa is often described as a largely import-dependent continent. The continent also requires significant investment in information and communication technologies (ICT) and renewable energy to achieve low-carbon economic growth. However, empirical evidence on the joint impacts of imports of goods and services, clean energy use, ICT, and economic growth on carbon emissions in Africa is scanty and mixed. This paper investigated the impacts of information and communication technologies, renewable energy consumption, import and economic growth on carbon emissions by using rich data on total per capita carbon dioxide (CO2) emissions, economic growth, import of goods and services, renewable energy consumption, fixed telephone subscriptions, mobile cellular subscriptions, and individuals using the internet in Africa (2001 ─ 2020) obtained from the World development indicators (WDI) database. Using the Panel autoregressive distributed lag model (PARDL), we found that mobile cellular subscriptions, and level of economic growth significantly increased per capita CO2 emissions in Africa in the long run while renewable energy consumption and technologies and import of goods and services significantly decreased per capita CO2 emissions in the long run. We conclude that information and communication technologies, level of economic growth, import of goods and services, and renewable energy consumption exert impacts on carbon emissions in Africa.

Purpose – To cope with climate change and achieve the dual carbon goal, China has actively promoted the implementation of carbon trading pilot policy, among which the power industry plays an important role in China’s carbon emission reduction work. The purpose of this paper is to study the influence of carbon trading policy on the energy efficiency of power industry and achieve the comprehensive goal of carbon emission reduction, carbon peak and carbon neutralization. Design/methodology/approach – This paper constructs the difference-in-differences model based on 2012–2019 provincial data to study the impact of carbon trading policy on energy efficiency in the power industry and its effect path. Heterogeneity analysis was conducted to compare the effects of carbon trading policy in eastern, central and western regions as well as at different levels of power structures. Findings – Carbon trading policy can significantly improve the energy efficiency of the power industry, and the policy effect is more significant in eastern and western regions and areas with high power structure. Mechanism analysis shows that carbon trading policy mainly influences the energy efficiency of power industry by environmental protection investment, power consumption demand and industrial structure. Originality/value – This paper uses provincial panel data to deeply study the influence of carbon trading policy on energy efficiency of the power industry and its effect path. By constructing the difference-in-differences model, this paper empirically analyzes the governance effect of carbon trading policy. Meanwhile, it controls individual and time effects to solve the endogeneity problem prevalent in previous literature.

Achieving spatiotemporal balance between carbon emissions and sinks at multiple spatial scales is vital for addressing climate change globally. The investigation of carbon budget variation and carbon balance zoning at the intra-city scale, however, has not received much scholarly attention. By integrating firm-level big data with other datasets, this paper aims to simulate high-resolution carbon emissions and sinks and to reveal the spatiotemporal variation of carbon budgets within Nanjing, one of metropolises in eastern China, between 2010 and 2019. We then formulated a carbon balance zoning scheme for 111 township-level regions of Nanjing based on the evaluation indicator system and clustering methods. Empirical results imply that the spatial pattern of carbon emissions has experienced a reshaping tendency from core-periphery to polycentric structure, while the scale and geography of territorial ecosystem carbon sinks remained stabilize during the past decade. There existed a spatial agglomeration effect in functional zones oriented by intra-city carbon balance of Nanjing. These are tied to determinants such as urban expansion, population redistribution, industrial restructuring, as well as regulations on energy use, natural resource supervision and ecosystem protection. The newly built urban sub-centers and new-towns in Nanjing were spatially overlapping with regions where significant changes in carbon budgets occurred. Our findings suggest that the current system of territorial governance should be reformed to support the adoption of major function and carbon balance zoning oriented by carbon peaking and neutrality at finer geographical scales in China.

The study analyses empirically the role of FDI as an interactive factor in the democracy, corruption, economic growth, ICT and carbon emissions nexus in sub-Saharan Africa (SSA). We use the Method of Moments Quantile Regression (MMQR) technique with data from 37 SSA countries covering the period 1970–2019. We find evidence that corruption and democracy create a significantly negative association with carbon emissions across most quantiles for the SSA countries. Conversely, economic growth, ICT and FDI develops a significantly positive relationship with environmental quality. Thus, the paper confirms validity of the Pollution Haven Hypothesis in SSA countries. More results demonstrate that the interaction variables, corruption × FDI, democracy × FDI, economic development × FDI also produces positive and significant links with carbon emissions, but the link between ICT × FDI and emissions is insignificantly negative. In conclusion, our findings indicate the need for a targeted approach towards improving FDI to fight corruption, enhance democracy, ICT and economic growth in pursuit of green economy and sustainable development in SSA.

With China’s rapid industrialization and urbanization in the process of socio-economic development, the extensive use of energy has resulted in a large amount of CO2 emissions, which puts great pressure on China’s carbon emission reduction task. Through multivariate socio-economic data, this paper proposes an extraction and screening method of multivariate variables based on land-use types, and the downscaled spatial decomposition of carbon emissions at different scales was carried out by using the spatial lag model (SLM). This paper makes up for the shortcomings of previous studies, such as an insufficient modeling scale, simple modeling variables, limited spatio-temporal span of spatial decomposition, and no consideration of geographical correlation. Based on the results of the spatial decomposition of carbon emissions, this paper explores the spatial and temporal dynamics of carbon emissions at different scales. The results showed that SLM is capable of downscaling the spatialization of carbon emissions with high precision, and the continuity of the decomposition results at the provincial scale is stronger, while the differences of the decomposition results at the municipal scale are more obvious within the municipal units. In terms of the spatial and temporal dynamics of CO2 emissions, carbon emissions at both scales showed a significant positive correlation. The dominant spatial correlation types are “Low–Low” at the provincial level, and “Low–Low” and “High–High” at the municipal level. The smaller spatial scope is more helpful to show the geographic dependence and geographic differences of China’s carbon emissions. The findings of this paper will help deepen the understanding of the spatial and temporal changes of carbon emissions in China. They will provide a scientific basis for the formulation of feasible carbon emission reduction policies.

To solve various reworking and repair problems caused by unqualified bearing product quality inspections, this paper introduces a green re-entrant scheduling optimization method for bearing production shops considering job reworking. By taking into account quality inspection constraints, this paper establishes an integrated scheduling mathematical model based on the entire processing–transportation–assembly process of bearing production shops with the goals for minimizing the makespan, total carbon emissions, and waste emissions. To solve these problems, the concepts of the set of the longest common machine routes (SLCMR) and the set of the shortest recombination machine combinations (SSRMC) were used to propose the re-entrant scheduling optimization method, based on system reconfiguration, to enhance the system stability and production scheduling efficiency. Then, a multi-objective hybrid optimization algorithm, based on a neighborhood local search (MOOA-LS), is proposed to improve the search scope and optimization ability by constructing a multi-level neighborhood search structure. Finally, this paper takes a bearing production shop as an example to carry out the case study and designs a series of experimental analyses and comparative tests. The final results show that in the bearing production process, the proposed model and algorithm can effectively realize green and energy-saving re-entrant manufacturing scheduling.

In this paper, we develop a two-level supply chain model with low carbon emissions for defective deteriorating items under learning in fuzzy environment by using the double inspection process. Carbon emissions are a major issue for the environment and human life when they come from many sources like different kinds of factories, firms, and industries. The burning of diesel and petrol during the supply of items through transportation is also responsible for carbon emissions. When any company, firm, or industry supplies their items through a supply chain by using of transportation in the regular mode, then a lot of carbon units are emitted from the burning of petrol and diesel, etc., which affects the supply chain. Carbon emissions can be controlled by using different kinds of policies issued by the government of a country, and lots of companies have implemented these policies to control carbon emissions. When a seller delivers a demanded lot size to the buyer, as per demand, and the lot size has some defective items, as per consideration, the demand rate is uncertain in nature. The buyer inspects the received whole lot and divides it into two categories of defective and no defective deteriorating items, as well as immediately selling at different price. The fuzzy concept nullifies the uncertain nature of the demand rate. This paper covers two models, assuming two conditions of quality screening under learning in fuzzy environment: (i) the buyer shows the quality screening and (ii) the quality inspection becomes the seller’s responsibility. The carbon footprint from the transporting and warehousing the deteriorating items is also assumed. The aim of this study is to minimize the whole inventory cost for supply chains with respect to lot size and the number of orders per production cycle. Jointly optimizing the delivery lot size and number of orders per production cycle will minimize the whole fuzzy inventory cost for the supply chain and also reduce the carbon emissions. We take two numerical approaches with authentic data (from the literature reviews) for the justification of the proposed model 1 and model 2. Sensitivity observations, managerial insights, applications of these proposed models, and future scope are also included in this paper, which is more beneficial for firms, the industrial sector, and especially for online markets. The impact of the most effective parameters, like learning effect, fuzzy parameter, carbon emissions parameter, and inventory cost are shown in this study and had a positive effect on the total inventory cost for the supply chain.

Accounting for the embodied carbon in construction materials and calculating the carbon footprint of entire construction projects in life-cycle assessments is a rapidly developing area in the construction industry. Carbon emission accounting relies on inventories that claim to represent the values of carbon contained in materials. However, these values vary between different carbon inventories. This scoping review identifies academic research on the carbon inventories used in Australia, as well as the methods used to compare these inventories. The study was conducted in accordance with the JBI methodology for scoping reviews. We identified 182 papers and narrowed these down to 11 that complied with the objectives of this study. Data for a range of construction materials were compared in these papers, as were the methods used to calculate the values. While some carbon inventories were used frequently, no clear preference for the method of calculating carbon values was apparent. The system boundaries also varied between publications, and a range of functional units was used. There was agreement that the variables involved in calculating carbon values for building materials are compounded by the practical issues of extracting and manufacturing materials in different regional or local conditions, cultures, and technological situations. It is therefore understandable that different inventories store different values when so many factors need to be considered. There is thus a clear need for agreement to be reached about standardisation of the processes involved. If the trustworthiness of the data stored in carbon inventories is questionable, so too are the outcomes of subsequent activities.

This paper investigates the impact of climate change and technological innovation on economic growth, energy consumption, and carbon emissions by using annual observations of a wide range of variables of Asian and European countries from 1996 to 2021. The study uses a dynamic GMM model and reveals significant regional heterogeneity and time-varying effects of technological innovation on economic growth. Technological innovation plays a more substantial role in promoting sustainable economic growth in European countries compared to Asian countries. The study also uncovers a spatial linkage and dependence between climate change and technological innovation. The technological transfer from European countries has increased energy consumption and carbon emissions in Asian countries, leading to abnormal climate change. The paper underscores the importance of targeted technological innovation strategies in Asian countries to improve energy efficiency and mitigate climate change. The study also confirms the EKC hypothesis with an observed inverted U-shaped relationship between economic growth and carbon emissions.

Abstract The building sector is one of the three major energy consumption areas and one of the main areas responsible for carbon emissions. In 2019, carbon emissions related to construction and building operations in China accounted for 38% of the total social carbon emissions, of which construction accounted for 16% and operations accounted for 22%. Due to its large volume and high energy consumption per unit area, public buildings account for 38% of the operating energy consumption of all buildings, that is, 8% of the total national energy consumption. At this time, the building industry must take decarbonization actions to avoid a delay in realizing carbon neutrality and an emission peak. We need to form a unified process for the implementation boundary, implementation path, and index system to build a zero-carbon implementation plan for China’s public building sector. Based on bottom-up practical cases, this paper proposes the KAYA model, which is applicable to different scales and different types of public buildings/communities, and proposes specific and feasible plans. Through the implementation of demand reduction, energy efficiency improvement, and the fully-use of renewable energy in all five clear steps, this paper promotes the implementation of decarbonization in China’s building industry.

For reduce the carbon emissions of grain transportation, improve ment the efficiency of grain transportation and ensurance the safety of grain transportation, this paper constructed a location-routing optimization model of grain transshipment hub driven by low carbon according to the perspective of quantitative analysis. An improved genetic algorithm was designed to solve the model, which could improve and optimize the layout of China's existing grain transportation network. Finally, some provincial capital cities on the key line of grain logistics were selected for analysis and verification. The results showed that The model and algorithm of hub node sitting-route optimization in hub-spoke multimodal transport network could reduce the total grain transportation cost by 13.87%. Carbon emissions were reduced by 11.70%, and the average transfer saturation of hubs was increased by 9.66%. This method was suitable for the actual grain transportation process, and can realize the low-carbon and efficient transportation of grain, which could provide reference for the location and path planning of Chinese grain transport hub under low-carbon drive.

Abstract For many years Norway has been in the forefront of promoting electromobility. Today, Norway has the world’s highest per capita fleet of plug-in electric cars. In 2021, 1.6% of the cars in the EU fleet were plug-in electric vehicles, whereas their share was 21% in Norway. Part of the successful market take-up rate is due to wide-ranging tax exemptions. Increasing plug-in electric vehicles numbers causes tax revenue losses, making exemptions unsustainable. Norway has the ambitious goal that from 2025, all newly registered cars shall be zero-emission vehicles. Keeping tax exemptions in place might be crucial for this goal. The objective of this paper is to provide information to solve this dilemma. Tax exemption reduction and abolition paths which offer a compromise between minimal effects on the development of zero-emission vehicles and tax revenues have been identified. An updated and re-calibrated version of the stock-flow-model SERAPIS was used to simulate and assess different scenarios. Results show that a controlled tax phase-in allows Norway to reach its environmental targets of 100% zero emission vehicles by 2025 and a 55% decrease of CO2-emissions in 2030 relative to 2005 while simultaneously increasing public revenues significantly.

Abstract In this paper, we selected representative electric buses (EBs) and diesel buses as research cases, aiming to explore a localized method suitable for various vehicle types that can be implemented in any region to measure the carbon emissions of urban buses throughout the lifecycle. We take into account the influence of seasonal factors over the lifecycle. We fully consider the production, assembly, and recycling stages of vehicle systems. We construct a model for estimating CO2 emissions during the fuel cycle. We analyze and compare the impacts of the optimized power grid structure on the lifecycle CO2 emissions of the two types of buses. The results indicate that seasonal factors affect both bus types' energy consumption, with EBs being more sensitive. Carbon emissions primarily stem from manufacturing the body, chassis, and notably, the lithium‐ion batteries, with the latter having a pronounced impact on EBs. A decrease in coal power generation from 71% to 21% in the grid reduced EB fuel‐cycle emissions by 70.7%. Although EBs are sensitive to seasonal factors, they still have the greatest potential for carbon reduction, depending on the proportion of clean energy in the power grid and the production processes of vehicle components.

The authors of the paper aim to highlight an evolution of the agricultural system from the per- spective of sustainability in the Common Agricul- tural Policy. The purpose of this article is to make a systemic analysis of the convergence of the agri- cultural economy in relation to the realization of the assessment of agricultural sustainability. An essential premise for the application of the metho- dological analysis was represented by an asses- sment of agriculture in relation to the acceleration by agricultural systems of the conditions imposed as targets of eco-conditionality. The information was analysed and extracted from the available sta- tistical databases: Agridata and Eurostat. So, the idea of conditionality regarding the sustainability of the agricultural system is underlined by the implications in the implementation of agricultural decarbonization policies. The conclusion outlined by the authors is that agricultural management can lead to the optimization of biodiversity protection as a means of agricultural sustainability

Abstract The electric power industry is a key industry for the country to achieve the double carbon target. Its low carbon development has a double effect on this industry and helps other industries to achieve the carbon peak target. This paper firstly uses the IPCC inventory method to calculate carbon emissions in the production phase of the power industry in Gansu Province from 2000 to 2019, followed by the ridge regression method and the STIRPAT model to analyse the quantitative impact of six major drivers on carbon emissions, and finally, the scenario analysis method is used to forecast carbon emissions in this phase. The results show that the carbon emissions of Gansu Province show a trend of rising and then falling, and reached a peak of 65.66 million tons in 2013. For every 1% increase in population effect, urbanisation level, affluence, clean energy generation share, technology level and industrial structure, carbon emissions will grow by 4.939%, 0.625%, 0.224%, − 0.259%, 0.063% and 0.022% respectively. Because of the clean energy advantage in Gansu Province, the low-carbon development scenario will continue to have low carbon emissions during the scenario cycle, which can be reduced to 53.454 million tons in 2030; the baseline scenario will achieve a carbon peak in 2025, with a peak of 62.627 million tons; the economic development scenario has not achieved carbon peak during the scenario cycle, and carbon emissions will increase to 73.223 million tons in 2030.

Abstract This paper analysed the causal link between education, employment, renewable energy consumption and carbon emissions in Africa, where there is scant evidence. Relying on panel data obtained from the World Development Indicators for thirty-two African countries covering a period of 19 years, and five panel rigorous regression models, we found that renewable energy, investment in education, school enrolment, net national income per capita, and employment had negative and significant effects on carbon emission, thus increasing these predictors would result in significant reduction in carbon emission in Africa. We identified a bidirectional causality between carbon emissions and net national income per capita, education expenditure and renewable energy consumption, and carbon emissions and employment. Our findings suggest that investment in education, renewable energy, and employment are relevant in mitigating carbon emissions in Africa. We recommend African governments to invest heavily in education, improve school enrolment, environmental education, renewable energy and employment provision to mitigate carbon emissions. Graphical Abstract

The growing threat of climate change, driven largely by carbon emissions, has made the transition to net-zero carbon emissions a critical global priority. In this systematic literature review conducted between 2021 and 2024 within the oil and gas domain, the investigation focuses on the pathway toward achieving net-zero carbon emissions. The study focuses on the role of carbon sustainability reporting, specifically those aligned with the Greenhouse Gas (GHG) protocol framework, in facilitating this transition. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this research thoroughly examines 34 papers specifically published in the oil and gas sector. This rigorous approach yielded 96 relevant studies encompassing a diverse range of industries. The analysis delves into the adoption, implementation, and impact of carbon sustainability reporting practices that adhere to the GHG Protocol Framework. The findings identified key themes within the collected research, including the effectiveness of carbon sustainability reports in driving organizational practices towards carbon neutrality, the challenges encountered on the path to net-zero carbon emissions, and the broader implications for policy development and future research directions. By synthesizing the findings from this extensive selection of studies, this review offers a comprehensive role of the carbon sustainability reporting within the oil and gas industry based on the GHG protocol framework. Furthermore, it proposes valuable recommendations for advanced initiatives towards a carbon-neutral future.

Steel production is a critical economic activity and amongst the largest industrial consumers of energy. The industry faces a complex and costly task to decarbonise in line with global climate targets. This paper evaluates the performance of environmental and emissions scores within leading Environmental, Social and Governance (ESG) ratings products in capturing carbon emissions outcomes and investment in low-carbon production amongst major steel producers. We assess data for 75 steel producers, representing 65% of global production. We find no strong evidence that environmental or emissions scores reflect either levels of, or changes in, firms’ total greenhouse gas emissions or emissions intensity in the period 2013–2022. Overall, ‘good’ scores are not explained by available emissions or investment data. These findings for a critical industry emphasise the need for methodological transparency from all ratings providers, more research into ratings’ performance in reflecting outcomes and investments, and further policies to enhance disclosures from firms and rating agencies.

For sustainable energy management, soft computing can play an important role in developing a charging station planning for low carbon transition to address the challenges faced by electric vehicle (EV) infrastructure system. This article proposes a planning scheme for charging stations of electronic vehicles for low carbon transition using soft computing-based computational techniques in the transportation sector. The existing charging stations have many flaws which are acting as hindrance in the development and acceptance of electronic vehicles by consumers. These flaws include the unreasonable layout of charging terminals, difficulty in charging, consumption of time in charging, and consumer convenience. This paper proposes a novel planning model for charging electronic vehicles as an innovative solution for electronic vehicles for low carbon transition. The protection of the environment is important in this current era and switching to electronic vehicles is a mandate for low-carbon transitions in the transportation sector. The proposed method begins with the collection of data from diverse sites of charging stations using the existing charging infrastructure, and this data also analyzed for ascertaining the energy consumption patterns, availability of renewable energy options, flow of traffic, and consumer preferences. The proposed method aims to augment the deployment of convenient charging infrastructure to promote consumers to switch to electronic vehicles from fuel-based vehicles and to promote renewable energy solutions for electric vehicles (EVs) at economic costs. The location and capacity of charging terminals is also optimized with the aid of hybrid algorithm based on catfish and PSO algorithms in this proposed research work. The effectiveness of the proposed soft computing-based hybrid algorithm is tested by using standard statistical methods and respective results are presented in the result section of this article. The proposed research has practical implications in a real-world scenario where consumers can be promoted to use electric vehicles by optimizing the capacity and availability of the charging station infrastructure which can eventually reduce the carbon footprints from this world.

AbstractPrevious research has explored the relationship between carbon emissions, trade openness, and foreign direct investment (FDI), but these studies have not specifically examined carbon emissions using sector-level data. This paper expands upon the existing body of literature by employing a threshold regression approach, utilizing the intensity of carbon emissions as a primary variable to scrutinize the effects of FDI and trade openness on carbon emissions at a sectoral level. Our findings indicate that the impact is contingent upon the chosen thresholds, thereby underscoring the influence of foreign trade openness and FDI on carbon emissions within the industrial sector. The effect of FDI on sector-specific industrial carbon emissions is not constant, with the influence coefficient varying over time. In contrast, trade openness positively and negatively impacts carbon emissions. Specifically, increased foreign trade openness leads to a decrease in carbon emissions in less carbon-intensive sectors. Factors such as the intensity of economic activity, employment levels, independent technical innovation, and per capita GDP significantly influence carbon emissions within industrial sectors.

The high-quality development of green economy is an important way for the future development of China’s economy, but the academic research is relatively few. Based on the panel data of 30 provinces (cities and districts) in China from 2003 to 2019, using Moran index, Spatial Dobbin Model (SDM), intermediary effect and so forth, this paper constructs an evaluation index for the high-quality development of green economy. This paper tries to answer the impact of green credit, carbon emissions and the high-quality development of the green economy and some other important questions. Answering the above questions can effectively help government departments to better formulate policies to achieve long-term development goalsThe results are as follows: the improvement of the level of green credit is conducive to promoting the high-quality development of green economy, and the rise of high-quality development level of green economy in a certain region will obviously drive the high-quality development level of green economy in adjacent regions. Meanwhile, carbon dioxide emission plays an intermediary role between green credit and high-quality development of green economy. The impact of green credit on the high-quality development of green economy is more obvious in areas with high economic development level. Moreover, with the improvement of high-quality development of green economy, the promotion effect of green credit is increasing. This research fills the gap in this field at home and abroad and provides ideas and suggestions for the future development of China’s economy.

Although Enhancing green total factor productivity (GTFP) within the agricultural sector is crucial for fostering sustainable development. In this paper, the GTFP of China’s maize industry is analyzed using the SBM-GML index method, considering data from the primary maize-producing provinces from 2004 to 2020. This analysis incorporates carbon emissions as undesirable outputs. The spatial Durbin model aids in investigating the factors influencing maize GTFP. Our findings reveal a positive trajectory for China’s maize GTFP over the designated period, featuring an average yearly increase of 0.8%. This ascension is primarily attributed to advancements in green maize technology. In the key cultivation regions of the Yellow and Huaihai areas, the Northern region, and the Southwest region, the average annual growth rates were 1.5%, 0.87%, and 0.09%, respectively. Among the direct influences, variables such as regional human capital, the extent of maize cultivation area, financial assistance towards agriculture, and the degree of agricultural mechanization considerably bolster the optimization of maize GTFP. Conversely, urbanization and the prevalence of natural disasters in the agricultural sector pose substantial challenges to enhancing maize GTFP. Furthermore, the spatial spillover effects reveal that natural agricultural disasters in a particular region inadvertently contribute to the improvement of maize GTFP in adjacent regions. Additionally, the regional human capital levels can significantly impede the progress of maize GTFP in neighboring regions. Therefore, to ensure food security, it is imperative to actively advocate for green development within the maize industry to Enhancing green total factor productivity (GTFP) in agriculture is crucial for agriculture to promote sustainable development. In this paper, using data from 2004-2020 from China’s main maize-producing provinces, the SBM-GML index method is used to measure China’s maize GTFP, and the spatial Durbin model is applied to examine the influencing factors and spatial spillover effects of China’s maize GTFP growth. The results of the study revealed a positive trajectory of Chinese maize GTFP over the specified period, with an average annual growth of 0.8%. This enhancement is mainly attributed to the progress of green maize technology. The average annual growth rates were 1.5%, 0.87%, and 0.09% in the major cultivation areas of the Yellow and Huaihai regions, the northern and southwestern regions, respectively. The analysis of influencing factors showed that among the direct influencing factors, variables such as regional human capital, extent of maize cultivation area, financial assistance to agriculture and degree of agricultural mechanisation contributed significantly to the optimization of maize GTFP. Conversely, urbanisation and the prevalence of natural disasters in the agricultural sector pose significant challenges to improving maize GTFP. Furthermore, spatial spillovers reveal that natural agricultural disasters in a given region unintentionally contribute to the improvement of maize GTFP in neighbouring regions. Furthermore, regional human capital levels can significantly hinder progress in maize GTFP in neighbouring regions. Therefore, in order to ensure food security, the greening of maize production must be actively promoted.

The rapid expansion and implementation of digital technologies are profoundly changing our lifestyles, production methods, and modes of exchange. However, whether or not such change can help promote the sustainability of urban socio-environmental systems has not been fully investigated. In this paper, we propose a comprehensive framework that utilizes the spatial Durbin model to investigate whether and how the digital economy contributes to low-carbon urban development, using the panel data of 276 Chinese cities from 2011 to 2018. This framework systematically identifies triple dimensions of low-carbon transition taking place throughout the whole value chain system, i.e. green innovation, production reshaping, and consumption upgrading. The finding reveals that the digital economy in a city exerts a significant mitigating effect on carbon emissions in both local and neighboring cities. It is also observed that digital economy activities play a significant role in alleviating urban carbon emissions due to their capacity to stimulate green innovation and promote sustainable patterns of production and consumption. The robustness of these findings is assessed through the consideration of endogeneity and substitution of the explained and explanatory variables. This paper contributes to a systematic understanding of the linkage between the revolution of digitalization within economic systems and the transition towards sustainability within environmental systems. It also provides valuable insights to policymakers on strategies to promote and accelerate low-carbon development.

Conservation and efficient use of water resources are key to combating global climate change. The development of low-carbon industries plays an important role in promoting the protection of water resources. Based on this, this paper combines the measures of green production and consumption, and uses the concept of low-carbon packaging design to study the impact of water resource protection. First of all, this paper studies the impact of the packaging industry on the ecological environment, and combines the concept of global low-carbon development to implement a low-carbon packaging industry structure. Secondly, adhering to the concept of sustainable development, from green packaging design to green packaging consumption, this paper uses the water resources protection analysis impact model to empirically analyze the impact of the low-carbon packaging industry on water resources protection. The experimental results showed that after the implementation of the low-carbon packaging industry structure model in Zhejiang Province, China, the impact efficiency of the region on water resources was as high as 90%, and the sewage discharge showed a decreasing and stable trend, sewage discharge in the light industrial zone was reduced from 120,000 to 80,000 cubic meters, and two water pollutants, chemical oxygen demand (COD) and biochemical oxygen demand (BOD), were reduced by 4,600 and 2,200 tons respectively. Studies have shown that low-carbon packaging design can reduce carbon emissions from packaging production to a certain extent and reduce pollutant emissions, thereby improving the efficiency of water resource protection and management.