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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.

Graphical abstract Energy and environmental policies are important methods for the government to restrain carbon emissions growth. Identifying the potential dynamic trends of China's carbon emissions under different scenarios has important reference significance for the government's policy implementation. This paper firstly predicted China's carbon emissions from 2017 to 2040 based on three energy transition scenarios at the industrial level. Then, Logarithmic Mean Divisia Index decomposition model was applied to evaluate the driving forces of emissions changes during 1997–2040. Finally, the Spatial–Temporal Logarithmic Mean Divisia Index model was used to explore the emissions reduction potential and the potential reduction path at provincial level. The results showed that (1) as the reduction in energy intensity cannot offset the growth of industrial scale, the carbon emissions of all industries have shown an increasing trend from 1997 to 2017; (2) In the current policies scenario, China's carbon emissions cannot reach the peak before 2040. And only in the sustainable development scenario, the carbon emissions of the three industries will all reach the peaks before 2030. And the development of non-fossil energy will reduce carbon emissions by more than 30%; (3) Hebei, Shanxi, Inner Mongolia, Ningxia, and Heilongjiang are key provinces and improving energy efficiency of the secondary industry is a potential way to promote carbon emissions reduction. The framework and main content of this paper. Supplementary Information The online version contains supplementary material available at 10.1007/s10098-021-02240-7.

Does regional corruption exacerbate regional carbon emissions? To answer this, based on the spatial Durbin model, this study empirically examines the impact of regional corruption on carbon emission, using panel data from 30 provinces in China during the period 2002–2017. The results show that: (1) there is an indistinctive N-shaped relationship between regional corruption and carbon emissions at the national level. Regional corruption tends to initially aggravate carbon emissions, then contributes to emission reduction, and then finally boosts carbon emissions. However, this effect is not statistically significant. The results suggest that the role of regional corruption on carbon emissions is twofold. Corruption can exacerbate and can also inhibit regional carbon emissions. (2) Pronounced regional heterogeneity exists with regard to the influence of corruption on carbon emissions. Regional corruption and carbon emissions show a significant N-shaped dynamic relationship in China’s central region, while the relationship is not significant in the eastern and western regions. (3) The impact of regional corruption on carbon emissions varies with time. For 2002–2009, regional corruption did not have a significant effect on carbon emissions. For 2010–2017, the direct effect became significant, and an apparent N-shaped relationship formed between regional corruption and carbon emissions. Based on the empirical results, this paper proposes several policy recommendations regarding corruption and carbon governance. Graphic abstract

Thirty years ago, Adams (1981) depicted a future UK where everyone was a millionaire lorry driver, simply by extrapolating from contemporary official transport growth assumptions. These assumptions underpinned the ‘predict and provide’ approach which then characterised transport planning. Twenty years later, the New Deal for Transport White Paper (1998) abandoned ‘predict and provide’ as unsustainable. This paper argues that the same growth assumptions that Adams took to their logical (absurd) conclusion have re-emerged to define both transport and the drivers of transport demand. While non-aviation transport is supposed to be carbon-neutral by 2050, the implied reductions in emissions rely on an absolute decoupling of transport demand and its drivers for which there is no evidence in current planning. Targets rely on optimistic, narrowly framed technology forecasts and behaviour change assumptions which appear highly unlikely in the present socio-political climate. Moreover, such is the cost of mitigating these tensions between economic growth and other concerns, it is argued that the targeted outcomes of current policy are as undesirable as they are unlikely. The paper concludes by calling for a transport policy which considers mobility in an integrated, holistic fashion, rather than merely as a dimension of economic growth.

ConCave Ph.D. Symposium 2022 Proceedings, April 7-8, 2022. Georgia Institute of Technology, Atlanta, Georgia., Pakistan is facing the issue of energy shortage in the housing sector for the past two decades, with power outages throughout the country all year long. Increased rate of population growth and increased urbanization rate are the factors responsible for this issue, regarding the future of the energy supply in the urbanized regions of Pakistan.The urban housing sector has also emerged as one of the main sources of carbon emissions in the country and has become a serious concern from an environmental point of view. The housing sector is projected to becomethe biggest consumer of the two main energy sources (electricity and gas). The main objective of this paper is to carry out a review of literature focusing on the housing sector, in terms of energy consumption and coevolution of the spatial layout of the residential sector in Pakistan. As the paper is part of an ongoing Ph.D. research; therefore, the outcome of the paper was highlighting the issue of excessive use of energy in the single-family housing sector of Pakistani urban areas for building a foundation for further investigation of the reason behind excessive use of energy in this housing sector.

China, as the world's largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities, must enhance efforts in carbon emission reduction to jointly realize China's low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that contribute to carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits carbon emissions, followed by energy structure effect. This paper considers the driving factors of both the production side and the residential consumption side from the city level. The research reveals the main driving factors that effect the carbon emissions of megacities in developing countries, and highlights the leading role of megacities in terms of carbon emission reduction in China and even the world. The paper thereby puts forward policy implications for China's economic policies.

Although the special economic zones (SEZs) are considered the backbone of rapid economic development in China, it is unclear whether they contribute to green economic development. From the perspective of the localized industrial chains formed as a result of the SEZ policy, this paper aims to analyze and explain how the development of SEZs influences carbon emissions in Chinese cities by promoting green technologies’ vertical spillover along the industrial chain. Based on the panel data of 264 prefecture-level cities from 2011 to 2016 and a relatively new and mostly disaggregated city-level MRIO (multi-region input–output) table in China, this paper constructs green technology vertical spillover as a mechanism variable and discusses the influence theoretically and empirically. The results show that the development of SEZs can reduce a city’s carbon emissions. More specifically, for every 10 m2 increase in the size of the SEZ area, the carbon dioxide emission can be reduced by 0.882 g per m2 of the city area. Moreover, mechanism analysis shows that the development of SEZs promotes green technology vertical spillover inside the city, through which the SEZs reduce the city’s carbon emissions. The mediation effect occupies 21.96% of the total effect. Furthermore, the impact of the development of SEZs on carbon emissions has regional heterogeneity due to the city’s industry structure, green technology stocks, and the zones’ administrative hierarchies. The finding of this study could provide several important implications for regional green development, especially in China.

Online grocery shopping is an emerging market. It caused visible growth in last mile delivery services, which has resulted in concerns about its external effects (e.g., pollution, land use, congestion). The study outlined in this paper proposes a grocery delivery concept where goods are transported by train to the customer’s nearest station, and then the consumer either picks up the groceries (i.e., click and collect) or the goods are delivered to the customer’s home, or to a locker. The focus of this paper is primarily based on the last mile delivery part of the supply chain (i.e., from the train station to the customer). The land use efficiency and emissions of each delivery concept have been evaluated based on the time-area concept and the Handbook Emission Factors for Road Transport (HBEFA 4.1), respectively. This large-scale simulation considers every household in Switzerland with various levels of demand and supermarket network densities. Two machine learning techniques (i.e., random forest and decision tree) have been used to categorise all neighbourhoods within Switzerland based on the best delivery method in terms of emissions and land efficiency. The results show that, depending on the scenarios (e.g., 10 or 100% of the households taking part), home delivery can be better for 76–89% of these communities when compared with a click and collect option based on their land use efficiency.

Due to the problems of economic structure and the ways of industrial development, many countries have accumulated many ecological problems in the process of economic development, especially the increase in carbon emissions, the greenhouse effect, and the emergence of a series of problems, which makes the global ecosystem suffer severe challenges. Achieving green sustainable development has become a strategic development arrangement for all countries, and as the sports economy is closely linked to regional green development, it has become an important channel to influence regional green development by promoting sports development. Based on theoretical analysis, this paper empirically analyzes and tests the impact of government fiscal support for sports on regional carbon emissions and its mechanisms based on provincial panel data using baseline regressions, a mediated effects model, and a threshold model. We argue that the development of green and healthy sports can have a significant impact on regional green development and that government fiscal support for sports plays an important role in this process. We found that government fiscal support for sports can significantly reduce regional carbon emissions and thus promote regional green development. From the perspective of mechanism analysis, the government’s fiscal support for sports leads to the orderly development of the sports industry and its associated industries, which improves the rationalization of the regional industrial structure and thus reduces regional carbon emissions. Moreover, as the carbon productivity of the region continues to rise, the impact of fiscal support for sports on regional low-carbon development gradually deepens. This paper confirms the important role of the government’s emphasis on sports development in the process of achieving carbon peaking and carbon neutrality, verifies the mediating effect of industrial structure rationalization, and further quantifies the correlation through the threshold effect, extending the study of the influencing factors affecting regional carbon emissions.

Reinforcement learning (RL) methods for energy saving and greening have recently appeared in the field of autonomous driving. In inter-vehicle communication (IVC), a feasible and increasingly popular research direction of RL is to obtain the optimal action decision of agents in a special environment. This paper presents the application of reinforcement learning in the vehicle communication simulation framework (Veins). In this research, we explore the application of reinforcement learning algorithms in a green cooperative adaptive cruise control (CACC) platoon. Our aim is to train member vehicles to react appropriately in the event of a severe collision involving the leading vehicle. We seek to reduce collision damage and optimize energy consumption by encouraging behavior that conforms to the platoon’s environmentally friendly aim. Our study provides insight into the potential benefits of using reinforcement learning algorithms to improve the safety and efficiency of CACC platoons while promoting sustainable transportation. The policy gradient algorithm used in this paper has good convergence in the calculation of the minimum energy consumption problem and the optimal solution of vehicle behavior. In terms of energy consumption metrics, the policy gradient algorithm is used first in the IVC field for training the proposed platoon problem. It is a feasible training decision-planning algorithm for solving the minimization of energy consumption caused by decision making in platoon avoidance behavior.

With the proposal of China’s “carbon peak, carbon neutral” strategy, the increasing awareness of low carbon production among consumers, and the government’s introduction of carbon trading mechanism and low carbon consumption subsidy policies, enterprises are facing good opportunities for development. However, how the government can reasonably formulate low carbon policies and how enterprises can implement optimal low-carbon production decisions are still key issues in China’s low-carbon transition development. In this context, this paper is based on the carbon trading mechanism and carbon consumption subsidies. In this context, based on the carbon trading mechanism, this paper focuses on green production and green consumption, considers the impact of low-carbon consumer preferences and government subsidies on enterprises’ low-carbon production decisions, and uses the optimal theory to study the optimal pricing strategy and the optimal carbon reduction strategy. The study shows that the increase in carbon price has a positive effect on the increase in enterprise profit; the increase of carbon emission has a negative effect on the increase of enterprise profit and the high carbon price will intensify this effect. In addition, changing the intensity of government subsidies to consumers will lead to the change of enterprise carbon emission strategy. The study of this paper provides a certain reference for the government to reasonably formulate carbon trading prices and consumer low-carbon subsidies. In addition, considering consumer low-carbon preferences is also conducive to promoting green production practices of enterprises, thus promoting the realization of the carbon neutral strategy.

The dense population and the large amount of domestic waste generated make it difficult to determine the best route and departure time for waste removal trucks in a city. Aiming at the problems of municipal solid waste (MSW) removal and transportation not in time, high collection and transportation costs and high carbon emissions, this paper studies the vehicle routing problem of municipal solid waste removal under the influence of time-dependent travel time, traffic congestion and carbon emissions. In this paper, a dual objective model with the lowest total economic cost and the highest garbage removal efficiency is established, and a DCD-DE-NSGAII algorithm based on Dynamic Crowding Distance and Differential Evolution is designed to improve the search ability, improve the convergence speed and increase the diversity of the optimal solution set. The results show that: according to the actual situation of garbage collection and transportation, the method can scientifically plan the garbage collection and transportation route, give a reasonable garbage collection scheme and departure time, and effectively avoid traffic congestion time; Through algorithm comparison, the algorithm and model proposed in this paper can reduce collection and transportation costs, improve transportation efficiency and reduce environmental pollution.

As a major carbon-emitting country, there is an urgent need for China to reduce carbon emissions. Studying the carbon emission efficiency of each province helps us to learn about the characteristics and evolution of regional carbon emissions, which is important for proposing effective and targeted measures to achieve the carbon peaking and carbon neutrality goals. This paper measures the carbon emission efficiency of 30 Chinese provinces from 2006 to 2019 based on a three-stage SBM-undesirable model and explores external drivers using stochastic frontier models. The results of the SBM-undesirable model show that the inter-provincial carbon emission efficiency is unevenly distributed and shows a big difference. From the results of the stochastic frontier model analysis, external drivers such as the intensity of finance in environmental protection, the level of economic development, the industrial structure, the level of urbanization, the degree of openness and the level of science as well as technology innovation all have an impact on the emission efficiency. In terms of LSTM model prediction, the model shows an excellent fitting effect, which provides a possible path for carbon emission efficiency prediction. Finally, based on the empirical results and the actual situation of each province in China, this paper proposes relevant feasible suggestions.

In order to ensure the safety of cultivated land and promote urban productivity, the Chinese government began to promote intensive land use at the legislative level from 2014. At the same time, China faces problems of carbon emissions and energy, so we need to improve energy efficiency. Therefore, this paper aims to verify the spatial effects of intensive land use on energy efficiency of China from 2009 to 2018. We further use an index system to quantify intensive land use and use chain DEA (data envelope analysis) to quantify energy efficiency. This paper finds that: (1) intensive land use can significantly improve energy efficiency. A 1% increase in the level of intensive land use will increase energy efficiency by 1.3%. (2) The intensive use of land in one city will have a negative impact on the energy efficiency of surrounding cities. The reason is that the intensive use of land in a single city may lead to the transfer of energy-consuming industries to surrounding cities. (3) The impact of intensive land use on the energy efficiency of surrounding cities has negative threshold characteristics, and the negative impact will be weakened as the level of integration of the city increases.

Global warming – mainly caused by carbon emissions – is a major global challenge for human sustainable development. Carbon emission reduction and resource recovery from sludge treatment are critical to the carbon neutralisation of future wastewater treatment plants. This paper analyses the key elements of carbon emissions during sludge treatment and disposal, namely energy source carbon emissions, fugitive carbon emissions and carbon compensation. Of the four mainstream process routes analysed in this work, anaerobic digestion + dry incineration is identified as the route with the highest potential for reducing carbon emissions in the future. Finally, based on a review of current international research hotspots, the future development directions for sludge treatment and resource recovery are discussed. This paper thus provides a comprehensive understanding of the current sludge treatment processing routes and serves as a reference for process route selection and future research on carbon neutralisation. HIGHLIGHTS The key elements of carbon emissions during sludge treatment are analysed.; Four mainstream process routes of sludge treatment from the perspective of carbon neutralisation are analyzed.; The route with the highest potential for reducing carbon emissions is proposed.; Current international research hotspots of resource recovery from sludge are overviewed.; Innovative low-carbon technologies for resource recovery are expected.

In recent years, global warming has become increasingly severe, and the ecological and environmental problems facing mankind have become increasingly serious. As the main areas of transportation activities, cities are also the main places of carbon emissions. As a necessary condition for human’s daily-life travel, it is particularly important to calculate the carbon emissions from urban transportation. Due to the different characteristics of economy and population in different regions of a city, the carbon emissions of urban buses show different characteristics in terms of temporal and spatial distribution. The developments of science and technology promote the application of big data analysis to specific practical life, enabling people to research and solve problems from a new perspective. This paper uses the GPS data of urban buses in Sanya City, China, to identify operation conditions from urban buses, and calculates the distance and time under different conditions. Based on the measured data of carbon emissions, this paper visualizes the distribution characteristics of carbon emissions by density analysis; explains the time distribution characteristics by the visual analysis of carbon emissions in different time periods, working days and rest days, and different energy types; and illustrates the spatial distribution characteristics by the spatial distributions of carbon emissions from Sanya’s buses on working days and rest days, as well as in different routes, providing reference for a low-carbon development of urban green transport.

China has entered a new stage of integrated development of urban and rural areas under the constraints of scarce land resources and the need for high-quality economic and social development. While there is concern about the state and speed of urban–rural integrated development (URID), increasing attention is being paid to efficiency improvement. This paper comprehensively measures the efficiency of URID from the input–output perspective, taking into account the impact of carbon emissions; it also studies the efficiency of URID and its developmental spatiotemporal characteristics in 73 cities within three major city clusters in the Yangtze River Economic Belt (YREB) from 2010 to 2019, and analyzes the input–output optimization strategies for URID within each of these major urban systems. The results show that (1) the comprehensive efficiency evaluation system constructed by the study can more objectively reflect the state and trends of URID. From 2010 to 2019, the efficiency of URID in the three major city clusters in the YREB showed a downward trend; in cities with better economic development, the efficiency of URID was lower than in cities with average economic development, where carbon emission indicators showed a significant impact. (2) The spatial distribution of URID efficiency in the three major city clusters in the YREB follows an inverted “U” shape; URID efficiency in the urban agglomeration in the middle reaches of the Yangtze River (MRYRUA) is higher than in the Chengyu urban agglomeration (CYUA), where it is higher than in the Yangtze River Delta urban agglomeration (YRDUA). (3) The input redundancy rates are high in the indicators for culture, sports and media, energy conservation and environmental protection, urban and rural communities, and housing security expenditures. Carbon emission redundancy has a negative impact on efficiency in URID. Based on the high redundancy rates of each input–output indicator, this paper proposes methods to optimize the efficiency of URID in each of the three major city clusters and provides directional guidance for promoting the high-quality development of regional urban–rural integration.

Government intervention and structural transformation play an important role in both the economy and carbon emissions. Based on provincial panel data from China from 2003 to 2020, this paper employs econometric models to investigate the impact of government intervention and structural transformation on carbon emissions. In particular, structural transformation is divided into two indicators: The rationalization of the industrial structure and the upgrading of the industrial structure. According to the research findings, government intervention has significantly promoted carbon emissions and structural transformation has had dual effects on carbon emissions; meanwhile, the rationalization of the industrial structure has significantly increased carbon emissions, while the upgrading of the industrial structure has slowed down carbon emissions, with these findings passing the corresponding robustness test. The relationship between government intervention, structural transformation, and carbon emissions varies significantly over time and across regions. Further investigations revealed that government intervention and structural transformation have a significant impact on carbon emissions in various panel quantiles. Finally, the paper makes policy recommendations in order to provide empirical support for promoting China’s high-quality economic development and achieving the “double carbon” goal.

Adhering to green and low-carbon development is the only way to achieve sustainable economic development in China. High-speed railway (HSR) has the advantages of low energy consumption and low pollution, which is representative of the high-quality development of China’s transportation industry. Based on the panel data of 285 cities in China from 2004 to 2017, this paper explores the impact of HSR development on CO2 emissions using a difference-in-difference model. The results show that the opening of HSR can effectively reduce the level of urban CO2 emissions. After the endogeneity test and the robustness test, this core conclusion is still valid. The mechanism test confirms that the opening of HSR can reduce the level of urban carbon emissions by promoting the upgrading of industrial structures and improving the level of green technology innovation. Based on empirical results, this paper proposes that we should make rational use of the positive externalities of the HSR network to promote green development, intensive development, and innovative development.

Driven by the information technology revolution, using artificial intelligence to promote intelligent manufacturing while achieving carbon emissions reduction is increasingly the focus of international attention. Given this, based on the fact that China’s industrial manufacturing is more intelligent, this paper uses industrial sector data and robot data from 2000 to 2017 to examine the impact of intelligent manufacturing on industrial carbon dioxide emissions and to discuss its internal mechanism. The research found that intelligent manufacturing significantly inhibits carbon dioxide emissions in the industrial sectors. The emission reduction effect is more obvious in industries with higher carbon emissions and intelligence. The mechanism test shows that intelligent manufacturing mainly achieves industrial emission reduction by reducing fossil energy consumption in the production process and improving energy use efficiency. The research findings of this paper provide favorable evidence for using new technologies, such as artificial intelligence, to achieve carbon emissions reduction, and validate the importance of intelligent manufacturing in tackling climate change in the future. It provides an essential reference for developing countries to use artificial intelligence for their carbon emissions reduction goals.

Based on the land market transaction data and city-level carbon emission data of 282 prefecture-level cities from 2005 to 2018 in China Land Market Network, this paper discusses the effect of land resource misallocation on carbon emissions and its influencing mechanisms. The research finds that, the local government’s strategy of “seek development with the land” has made a large amount of urban construction land being allocated to the industrial field, leading to the price of industrial land to be underestimated and obvious land resource misallocation. The land resource misallocation has significantly increased the level of urban carbon emissions through mechanisms such as hindering the upgrading of industrial structure, restraining technological innovation and weakening the effect of economic agglomeration. Moreover, the results are still robust after replacing the core variable indicators, considering extreme values and controlling endogeneity. Additionally, further study finds that land resource misallocation not only evidently aggravates the city’s own release of carbon emissions, but also has a remarkable spatial spillover effect on adjacent cities. Meanwhile, except for small cities, the misallocation of land resources in Type-I large cities and above, Type-II large cities and Medium-sized cities noticeably exacerbates urban carbon emissions, and the effect increases with the upgrading of city size. Regionally, the land misallocation on carbon emissions has significantly increased the carbon emissions in the eastern and central regions but has no significant impact on the carbon emissions in the western region. Finally, the conclusion of this paper will have important practical significance for further promoting the standardization of China’s land market and realizing the green and high-quality development of the urban economy.

Using the panel data of 30 provinces in China from 1997 to 2015, this paper studies the impacts of urbanization on carbon emission. We use the entropy weight method to measure the weight of the indicator to evaluate four-dimensional urbanizations, including population, economic, consumption and living urbanization. In addition, we investigated the spatial correlation of carbon emissions, taking the spatial differences into consideration. The spatial Durbin model is finally selected to analyze the impacts of urbanizations on carbon emission. The conclusions are: Firstly, from the results of the panel data model, the four dimensions of urbanization all play a significant role in promoting carbon emissions in the whole regions. However, in eastern China, central China and western China, four dimensions of urbanization have different impacts on carbon emissions. Secondly, from Moran’s I of carbon emissions from 1997 to 2015 in China, we conclude that carbon emissions in China present a significant spatial aggregation. Thirdly, from the results of spatial econometrics model, population urbanization only promotes local carbon emissions. Economic urbanization and consumption urbanization promote local carbon emissions and reduce carbon emissions in its neighboring provinces. Living urbanization promotes both local carbon emissions and its neighboring provinces’ carbon emissions. This paper proposes some recommendations for the carbon emission decreasing during urbanization. First, establishment and improvement of coordination mechanisms and information sharing mechanisms across regions should also be considered. Second, control population growth reasonably and optimize population structure in order to achieve an orderly flow and rational distribution of the population. Third, the assessment mechanism of the local government should include not only economic indicators but also other indicators.

Against the backdrop of severe global warming, the low-carbon city pilot policy, with carbon emission reduction as its main objective, is an important initiative for China to fulfil its international commitment to carbon emission reduction and promote a green and low-carbon development strategy. However, none of the literature has yet evaluated whether the pilot low-carbon city policy promotes urban land use efficiency as a policy effect. In view of this, this paper measures urban land use efficiency from a low-carbon perspective using a global reference super-efficiency SBM model based on data from 186 prefecture-level cities in China from 2005–2017, and subsequently constructs a difference-in-differences method to test the true impact of low-carbon city pilot policies on urban land use efficiency and carbon emissions, and uses a propensity score matching method to test its robustness. It is found that: (1) the average level of urban land use efficiency in China is low and on a downward trend; (2) overall, cities are predominantly low-efficiency cities, with only the high-efficiency cities in Guangdong Province showing spatial agglomeration; and (3) the low-carbon city pilot policy reduces carbon emissions while also negatively affecting urban land use efficiency. Accordingly, this paper puts forward corresponding policy recommendations.

With the ratification of the Paris Agreement at the Paris Climate Conference, reducing carbon emissions has become a global interest. Coal is one of the main industries causing carbon emissions; thus, quantifying carbon emissions from coal mining is an important step in reducing these emissions. Firstly, based on the life cycle idea, in this paper, we define the Carbon Emission Boundary of the fully mechanized coal mining method. Secondly, the carbon emission accounting model (B-R model) of fully mechanized coal mining is established, which includes the total amount of carbon emissions and the carbon emissions of each mining link during the mining process. The Fifth-II mining area of the Jinda Coal Mine in Tengzhou City is taken as an example. We collect the relevant data on carbon emissions in the mining process of the Jinda Coal Mine, and the B-R model is used to obtain the carbon emissions in the mining process of this mining area. Finally, the feasibility of the B-R model is further verified according to the international authoritative carbon emission IPCC calculation method and the China Coal Production Enterprises Greenhouse Gas Emissions Accounting Methodology and Reporting Guide. The results show that the B-R model in this paper is feasible and that the greatest amount of carbon emissions arises from the coal breaking link and coal transportation, which provides a basis for other coal mines to calculate carbon emissions. The B-R model lays a foundation for coal mines to formulate a carbon emission reduction system.

The traditional carbon-based approach towards sustainability has long caused the concepts of green and sustainable energies to be used interchangeably. Recent studies have tried to advance this archaic view by considering more aspects of sustainability. However, almost all major studies have been concerned with only the economic and environmental aspects of electricity generation, whereas the concept of sustainability is beyond these two criteria. In this paper, we seek to provide a methodology for a more comprehensive definition of electricity generation sustainability based on the lessons learned from previous studies and additional metrics suggested by them. The main characteristics of select electricity generation technologies were studied, and their environmental, economic, social, and technical criteria as well as the uncertainties associated with them were selected as the four major factors in our paper. It has also been argued that the utilization of regional resources in addition to the inherent characteristics of electricity generation technologies is vital in providing a realistic view of sustainability. Of the sustainability assessment methods previously introduced, the Relative Aggregate Footprint (RAF) method was used in conjunction with the previously selected criteria as the basis of the study due to its ability to incorporate additional criteria and regional considerations. As such, the framework for sustainability assessment presented in this research accounts for major criteria identified in the literature and takes the available regional resources that affect the feasibility of each electricity technology into account. This study paves the way for the presentation of new guidelines for the creation of more comprehensive electricity generation sustainability measures to distinguish between the concepts of green and profitable vs. sustainable energies to support the development of sustainable energy portfolios.

Although China has a high rate of economic development, it still faces the problems of unstable industrial structure, low industrial level, and large carbon emissions, which pose huge challenges to China’s sustainable development. China is working hard to develop producer services to achieve industrial transformation and reduce carbon emissions. In this context, there is an extremely urgent need to conduct academic research on changes in producer service agglomeration and carbon emissions. Whether the producer service industry has agglomeration characteristics, and whether the producer service industry affects carbon emissions through multiple paths, are issues worthy of attention. Therefore, this paper takes China as the research area, selects the provincial administrative unit as the research sample, and conducts research on these issues by using exploratory spatial data analysis methods and various spatial econometric models under the guidance of the relevant theories of new economic geography. Our research finds that producer services have significant spatial agglomeration characteristics, and they also have significant spatial differentiation patterns. In addition, the agglomeration of producer services can significantly promote the reduction of carbon emissions and can show obvious spillover effects. Finally, in response to the research conclusions of this paper, we also put forward countermeasures and suggestions from the perspective of the common development of producer service industry and manufacturing industry, hoping to promote China’s transformation from industrial economy to service economy, to maximize the use of producer service industry accumulated dividends.

Under the pressure of low-carbon development at county level in China, this paper takes Jiangsu province as an example to analyze the relationship between economic growth and carbon emissions, aiming to provide a reference for the low-carbon development in Jiangsu and other regions in China. Based on the county-level panel data from 2000 to 2017, this paper uses the Tapio elasticity model and environmental Kuznets curve model, and focuses on the differences in regional economic development and the impacts of the 2008 global economic crisis. The results show that, in general, the decoupling effect of carbon emissions in Jiangsu counties has gradually increased during the study period. Since 2011, all counties achieved the speed decoupling, with more than half of them showing strong decoupling. The environmental Kuznets curves of carbon emissions in different income groups are established, and changed before and after the 2008 global economic crisis. In 2017, only 10 of the 53 counties were on the right side of the curve, realizing the quantity decoupling between the two. Therefore, to achieve a win–win situation between carbon emission reduction and economic growth, efforts should be made from the aspects of industrial structure and energy efficiency, and measures should be taken according to local conditions.

The tightening of the financing environment and global climate change have become urgent problems for high-quality economic development all over the world. Facing these challenges, the Chinese government is committed to alleviating regional financing constraints and setting carbon-emission reduction targets. However, are these measures effective for high-quality urban development? This paper attempts to use unbalanced panel data from 290 cities on the Chinese mainland from 2004–2017 to provide an answer to the problem using a scatter plot and the mediator effect model. Results show that: (1) financing constraints limit the funds required for urban development, which is not conducive to high-quality urban development, but high-quality urban development has the characteristics of “path dependence”; (2) In the context of environmental regulation, financing constraints are mainly enacted through reducing carbon emissions, which is inconducive to high-quality urban development. Carbon emissions are the transmission mechanism whereby financing constraints affect high-quality urban development; (3) Cities with large financing constraints have insufficient capital investment for high-quality urban development, and the aggravation of financing constraints has an increasingly obvious inhibitory effect on high-quality urban development. Moreover, due to the effect of the global economic crisis in 2008, the negative effect of financing constraints on high-quality urban development had the characteristics of U-shaped fluctuation. Thus, this paper believes that the implementation of China’s double carbon policy is at the expense of high-quality urban development, and there is a long way to go before high-quality urban development reaches later stages. Other countries should carefully weigh up the relationship between environmental pollution and economic development when facing financing constraints.

Life Cycle Assessment (LCA) is increasingly used as an early-stage design-decision tool to support choices of structural system. However LCA modellers must first make numerous methodological decisions, and the resultant wide variations in approach are often inadequately described by the modellers.\ud \ud This paper identifies, and quantifies, the three major areas of methodological variation. These are: temporal differences in the stages considered; spatial differences in the material boundaries; and physical disparities in the data coefficients. The effects are then demonstrated through a case study of a student residential building in Cambridge. The cross-laminated timber (CLT) structure is compared with concrete frame, steel frame and load-bearing masonry, considering the influence that varying the temporal boundaries, the data coefficients, and the spatial boundaries has on the choice.\ud \ud While for this building CLT is confirmed as the lowest impact material, the paper demonstrates that varying the methodological choices can change the results by an alarming factor of 10 or even more. The findings confirm the need for the utmost clarity and transparency with all LCA calculations. Making wider industry or policy decisions based on LCA results should be undertaken with extreme caution.

As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies.

Fleet electrification is one of the measures proposed for achieving climate neutrality in the coming years. The replacement of internal combustion engine vehicles with electric vehicles has a positive impact on carbon emission reduction in some countries. However, in countries highly dependent on fossil fuels, such a possibility requires examination with respect to the means of electricity generation and fuel mix used in their power systems. One such country is Poland, selected as an example of an economy strongly dependent on fossil fuels. The main objective of this paper is to investigate the impact of fleet electrification of an individual company located in Poland on the reduction of carbon emissions. The concept and calculations are based on historical data on the single-year mileage and fuel consumption of 619 cars used by this company. Even though the Polish power system is based on fossil fuels, fleet electrification could contribute to a reduction in carbon emissions of 24%. The decrease in operational costs by EUR 370 thousand/year is also significant. Apart from environmental and economic impacts, this paper provides valuable findings on the difference between catalogue and real-driving data application in the various analyses. With respect to Polish fuel mix in 2019, the application of data published by car producers shows that fleet electrification would increase carbon emissions by 14% in this company. This means that depending on the initial assumptions, different conclusions can be drawn by policymakers, regulatory bodies, academics, or other groups of interest.

Owing to the advantages of high construction efficiency, prefabricated residential buildings have been of increasing interest in recent years. Against the background of global heating, designing low-carbon facades for prefabricated residential buildings has become a focus. The main challenge for this research is in designing windows for prefabricated residential buildings that can lead to the best performance in carbon emissions. The purpose of this paper is to summarize window design advice for prefabricated residential building facades with low-carbon goals. This paper adopts the single control variable research method. Building energy consumption and carbon dioxide emissions under different conditions comprise the primary data used in the study. In the process of achieving the research aim, this study firstly extracts the window design elements of prefabricated residential facades. Secondly, objective function formulas are established and a basic model is built for obtaining data. Thirdly, data results are analyzed and window design advice is put forward under the condition of a low-carbon goal. This paper discusses that the optimal window-to-wall ratio (WWR) with a low-carbon orientation is around 0.15, and compares it innovatively with the optimal WWR under an energy-saving orientation at around 0.38. The research results of this paper can deepen the understanding of architectural low-carbon design and play a guiding role for architects.

This paper proposes a mileage efficiency metric to enable direct comparison of energy efficiency and greenhouse gas emission of cars for various vehicle mass, fuel type, vehicle make, vehicle technology, road infrastructure, and drive schedule. This metric allows a unifying measure of vehicle mileage and fuel consumption regardless of the above factors. The proposed mileage efficiency metric is based on the concept of an ideal car (IC), which is defined as the one without internal or external losses; or an ideal powertrain car, which is defined as the one without internal losses. Mileage efficiency is defined as the ratio of a real-world car (RWC) fuel economy (in miles per gasoline gallon equivalent or mpgge) to that of an ideal car. As such, mileage efficiency is able to attribute efficiency loss of a RWC to internal and/or external losses. It is shown that mileage efficiency depends on car design and shows how good or bad RWCs are relative to ICs. The paper also highlights the importance of good road infrastructures in reducing transportation fuel consumption independent of fuel efficiencies of cars. This work provides a fundamental framework to set government policies and incentives for vehicle technology, alternative fuel choice, and infrastructure investments.

Due to increased global carbon dioxide emissions, the greenhouse effect is being aggravated, which has attracted wide attention. China is committed to promoting the low-carbon development of all industries. This paper analyzed the influencing factors of carbon emissions in the Chinese logistics industry, so as to identify the key factors that influence carbon emissions. Based on the carbon emission data of China’s logistics industry in 2000–2019, this paper applied the carbon emission coefficients issued by the Intergovernmental Panel on Climate Change. For the first time, the Generalized Divisia Index Method was used to analyze the degree of influence of the factors on carbon emissions. This method considered more variables and their relationships. The results showed that (1) the carbon emissions of the logistics industry were increased by 3.22 times from 2000 to 2018, and showed negative growth for the first time in 2019; (2) the added value of the logistics industry is the most important factor in increasing carbon emissions (with a contribution ratio of 65.45%), energy consumption and practical population size are the main factors in carbon emissions. The promotion of this industry is subjected to decreased per capita carbon emissions, which have a large impact on total carbon emissions; (3) the intensity of carbon output is the most important factor in the reduction of carbon emissions (with a contribution ratio of −29.1%), where the energy carbon intensity and per capita added value are the main influencing factors with regard to the reduction of carbon emissions, while energy intensity has a negative inhibitory effect on carbon emissions, and (4) the influencing factors have negative effects on the cumulative inhibition of carbon emissions in the logistics industry, to an extent that is far less than the integral promotion of carbon emissions. Finally, according to the research conclusions of this paper, it is feasible to make recommendations for the carbon reduction of the logistics industry.

open access article Organizations are under increasing pressure from governments and stakeholders to reduce carbon emissions from their business operations for climate change mitigation. Universities are not exempt from this challenge and are operating in a complex external environment, not least responding to the UK government’s Climate Change Act 2008 (80% carbon reductions by 2050 as per 1990 baseline). In 2012–2013, the UK Higher Education (HE) sector consumed 7.9 billion kWh of energy and produced 2.3 million tonnes of carbon emissions. This indicates the scale of the challenge and carbon management is central to reduce carbon emissions. However, effective processes for implementing and embedding carbon management in organizations in general, and universities in particular, have yet to be realized. This paper explores the critical success factors (CSFs) for embedding carbon management in universities and, more widely, in organizations. This exploratory study adopted a mixed-methods approach including the content analysis of universities’ carbon management plans alongside semi-structured interviews in the UK HE sector. The paper identifies six key factors for successfully embedding carbon management that are pertinent not just for the HE sector, but to organizations broadly: senior management leadership; funding and resources; stakeholder engagement; planning; governance and management; and evaluation and reporting.

In recent years, China has overtaken the United States as the world’s largest carbon dioxide (CO2) emitter. CO2 emissions from high-energy-intensive industries account for more than three-quarters of the total industrial carbon dioxide emissions. Therefore, it is important to enhance our understanding of the main factors affecting carbon dioxide emissions in high-energy-intensive industries. In this paper, we firstly explore the main factors affecting CO2 emissions in high-energy-intensive industries, including industrial structure, per capita gross domestic product (GDP), population, technological progress and foreign direct investment. To achieve this, we rely on exploratory regression combined with the threshold criteria. Secondly, a geographically weighted regression model is employed to explore local-spatial heterogeneity, capturing the spatial variations of the regression parameters across the Chinese provinces. The results show that the growth of per capita GDP and population increases CO2 emissions, by contrast, the growth of the services sector’s share in China’s gross domestic product could cause a decrease in CO2 emissions. Effects of technological progress on CO2 emissions in high-energy-intensive industries are negative in 2007 and 2013, whereas the coefficient is positive in 2018. Throughout the study period, regression coefficients of foreign direct investment are positive. This paper provides valuable insights into the relationship between driving factors and CO2 emissions, and also gives provides empirical support for local governments to mitigate CO2 emissions.

Cities, accounting for more than 3/4 of global final energy consumption, are equipping themselves with governance tools to improve energy efficiency. In Europe, urban energy policy has adopted, only recently and voluntarily, the Sustainable Energy Action Plans (SEAP), following the European Strategy 20-20-20. Italy, country most sensitive among European ones, accounts for 53% of SEAPs signatories. In order to evaluate how urban energy system in Italy can match sustainability European goals, it is necessary to analyse the technological options promoted by the energy policies for the urban environment. The paper presents the state-of-art of Urban Energy Planning in Italy, focusing on the implementation of Solar Energy technologies, and their role in new urban energy strategy instruments, i.e. SEAP, to promote renewables deployment. Carbon emission avoidance interventions planned by Italian big cities were analysed, highlighting the chosen Solar Energy technology. The aim of this paper is to discuss and evaluate the differences of solar energy harvesting in Italian urban scenarios, taking into account geographical and morphological constraints, and to compare the forecasts for 2020 and 2030scenarios, in accordance with European and National laws in force.

Purpose Environmental Management Accounting (EMA) information has become synonymous with win-win decision settings, but this paper aims to consider how EMA support can be extended to company managers who face the dynamics of win-wins and trade-offs. Design/methodology/approach Based on extant literature, the paper suggests an important extension of the use of EMA in support of management decision-making. The need for extended consideration and use of EMA to help overcome trade-offs is illustrated using the case of a wine bottling plant location decision by an Australian company in a global supply chain transporting wine from Australia to North America and Europe. Findings Results confirm the need to add to the broader use of EMA to assist managers attempting to solve real world trade-off problems between economic performance, carbon equivalent emissions reduction and water risk reduction. Research limitations/implications Generalisation of the single wine company case illustration to other companies and similar industry settings remains to be investigated. Practical implications Trade-offs are considered between economic benefit and two environmental performance matters of concern to the company, carbon equivalent emissions reduction and water risk reduction. Originality/value The paper introduces the notion of extending the use of EMA as a pragmatic way for managers to assess trade-off situations with environmental alternatives where no optimal solution is available. Value is added through the real case study of an Australian wine company.

The existing studies on persistence of ecological footprint have majorly concentrated on the aggregate ecological footprint without adequately considering its components. The aim of this paper is to contribute to the existing papers on ecological footprint by examining the persistence of carbon footprint emissions for a group of 92 countries. Unlike the extant papers, we use fractional integration, which allows us to consider the stationary I(0) and the nonstationary I(1) cases as particular models of interest, being therefore more general and flexible than these two classical representations. The results indicate that only 25 out of the 92 countries display mean reversion, with orders of integration strictly below 1 and showing transitory shocks, most of them belonging to lower-middle and low income countries. In these 25 countries it might not be easy to change the long run path of the carbon footprint as any policy shocks will have temporary effect. The foregoing findings can help policymakers in each nation to design efficient emission reducing policies. peer-reviewed 403 KB

This paper is an attempt to broaden economic discourse by importing insights into human behavior not just from psychology, but also from sociology and anthropology. Whereas the concept of the decision-maker in standard economics is the rational actor and, in early work in behavioral economics, the quasi-rational actor influenced by the context of the moment of decision-making, in some recent work in behavioral economics the decision-maker could be called the enculturated actor. This actor's preferences, perception, and cognition are subject to two deep social influences: (a) the social contexts to which he has become exposed and, especially, accustomed; and (b) the cultural mental models—including categories, identities, narratives, and worldviews—that he uses to process information. The paper traces how these factors shape individual behavior through the endogenous determination of preferences and the lenses through which individuals see the world—their perception and interpretation of situations. The paper offers a tentative taxonomy of the social determinants of behavior and describes the results of controlled and natural experiments that only a broader view of these determinants can plausibly explain. The perspective suggests more realistic models of human behavior for explaining outcomes and designing policies.

The building sector releases 36% of global CO2 emissions, with 66% of emissions occurring during the operation stage of the life cycle of a building. While current research focuses on using Building Information Modelling (BIM) for energy management of a building, there is little research on the visualizing building carbon emission data in BIM to support decision makings during operation phase. This paper proposes an approach for gathering, analyzing and visualizing building carbon emissions data by integrating BIM and carbon estimation models, to assist the building operation management teams in discovering carbon emissions problems and reducing total carbon emission. Data requirements, carbon emission estimation algorithms, integration mechanism with BIM are investigated in this paper. A case is used to demonstrate the proposed approach. The approach described in this paper provides the inhabitants important graphical representation of data to determine a buildings sustainability performance, and can allow policy makers and building managers to make informed decisions and respond quickly to emergency situations.

Carbon emissions calculation at the sub-provincial level has issues in limited data and non-unified measurements. This paper calculated the life cycle energy consumption and carbon emissions of the building industry in Wuhan, China. The findings showed that the proportion of carbon emissions in the construction operation phase was the largest, followed by the carbon emissions of the indirect energy consumption and the construction material preparation phase. With the purpose of analyzing the contributors of the construction carbon emissions, this paper conducted decomposition analysis using Logarithmic Mean Divisia Index (LMDI). The results indicated that the increasing buidling area was the major driver of energy consumption and carbon emissions increase, followed by the behavior factor. Population growth and urbanization, to some extent, increased the carbon emissions as well. On the contrary, energy efficiency was the main inhibitory factor for reducing the carbon emissions. Policy implications in terms of low-carbon construction development were highlighted.

Energy and facility operational costs are a large part of the overheads of Irish manufacturing and distribution companies. This paper deals with the design and implementation of low-carbon technologies and engineering innovations which have evolved over recent years, including integration of network-based programmable logic controllers (PLC), to enhance interconnectivity and control. Recording of the energy consumption of production plant and associated services, including heating and ventilation (HVAC) systems, compressed air systems, water usage/ recovery, power systems, information technology support systems and facilities were undertaken prior to commencing upgrade works. The paper describes how upgrade and retrofit works were identified following research into the latest equipment and controls, and these were undertaken as part of three separate projects on a phased basis. The initial project concentrated on simple achievable savings in common services for the complete facility, and included compressed air, exterior lighting, water heating and control of the main incoming water and gas services. Completion of the initial phase brought about an ongoing annual saving of 146,600kWh in energy consumption within the facility, reducing direct costs. The reduced energy consumption savings achieved from the first phase were used to part finance capital expenditure on the other two project phases, and combined with the timing of equipment upgrades and periodic maintenance, as in the case of lighting, over a three-year period. The second phase incorporated changes to the main heating and ventilation systems, along with IT infrastructure and centralised control of production processing machines. A major lighting upgrade to LED of the production and warehouse lighting fittings was undertaken as part of the third phase project, with occupancy controls integrated into zoned areas. Continuous monitoring and evaluation over the three years has enabled adjustments to systems, ensuring optimisation of savings and a further annual reduction of over 450,000kWh in energy. The savings have been achieved in a period of expansion in facilities and production, combined with a reduction in the maximum import capacity (MIC).

Solar and wind energy technologies, due to their nature of weather dependency, have been recognized as not the complete solution for the renewable energy transition. Creating a solution for the short fall is empirical if we are to remove the dependency on fossil fuels and reach net zero targets. The production of hydrogen, biogas and other gases can be produced sustainably, which can also allow for the utilization of waste materials or the ability to store energy and allow a greater positive impact on our environment. However, production of these gases is not always as transparent or environmentally friendly as perceived, so with the aid of certification and blockchain, we can create a system that can guarantee their environmentally positive origin, and ultimately help assist the transition to a greener future. This paper explores the varying production methods, with consideration to their environmental impact, and the implications of the use of certificates and blockchain to monitor production, trade and usage.

Lowering the embodied carbon dioxide equivalent (embodied CO2e) of buildings is an essential response to national and global targets for carbon reduction. Globally, construction industry is developing tools, databases and practices for measuring embodied CO2e in buildings and recommending routes to reduction. While the TC350 developed standardized methods for the assessment of sustainability aspects in construction works and Environmental Product Declarations, there is no consensus on how this should be carried out in practice. This paper evaluates the current construction industry practice through a review of both academic and professional literature, and through focus groups and interviews with industry experts in the field. Incentives in the available building codes, standards, and benchmarks are also analysed, as are the existing methodologies, tools and datasets. The multiple data sources are used to identify the barriers to the effective measurement and reduction of embodied CO2e in practice. This paper recommends that Governments mandate for improved data quality and support the development of a transparent and simplified methodology., EPSRC

Upcycling presents one of many opportunities for reducing consumption of materials and energy. Despite recent growth evidenced by increasing numbers of practitioners and businesses based on upcycling, it remains a niche activity and requires scaling up to realise its potential benefits. This paper investigates UK household upcycling in order to develop interventions for scaling up upcycling in the UK. Mixed methods were used in four stages: (a) Interviews to gain insights into UK upcycling, (b) a survey to discover key factors influencing UK upcycling, (c) intervention development based on the synthesis of interviews and survey, and (d) use of a semi-Delphi technique to evaluate and develop initial interventions. The results showed approaches to upcycling (e.g., wood, metal and fabric as frequently used materials, online platforms as frequently used source of materials), context for upcycling (e.g., predominant use of home for upcycling), factors influencing UK upcycling with key determinants (i.e., intention, attitude and subjective norm), important demographic characteristics considering a target audience for interventions (i.e., 30+ females) and prioritised interventions for scaling up (e.g., TV and inspirational media and community workshops as short-term high priority interventions). The paper further discusses implications of the study in terms of development of theory and practice of upcycling.

Under fierce market competition and the demand for low-carbon economy, cold chain logistics companies have to pay attention to customer satisfaction and carbon emissions for better development. In order to simultaneously consider cost, customer satisfaction, and carbon emissions in the cold chain logistics path optimization problem, based on the idea of cost&ndash, benefit, this paper proposes a comprehensive cold chain vehicle routing problem optimization model with the objective function of minimizing the cost of unit satisfied customer. For customer satisfaction, this paper uses the punctuality of delivery as the evaluation standard. For carbon emissions, this paper introduces the carbon trading mechanism to calculate carbon emissions costs. An actual case data is used with a cycle evolutionary genetic algorithm to carry out computational experiments in the model. First, the effectiveness of the algorithm and model were verified by a numerical comparison experiment. The optimization results of the model show that increasing the total cost by a small amount can greatly improve average customer satisfaction, thereby obtaining a highly cost-effective solution. Second, the impact of carbon price on total costs, carbon emissions, and average customer satisfaction have also been numerically analyzed. The experimental results show that as carbon price increases, there are two opposite trends in total costs, depending on whether carbon quota is sufficient. Increasing carbon price within a certain range can effectively reduce carbon emissions, but at the same time it will reduce average customer satisfaction to a certain extent, there is a trade-off between carbon emissions and customer satisfaction. This model enriches the optimization research of cold chain logistics distribution, and the study results complement the impact research of carbon price on carbon emissions and customer satisfaction. Finally, some practical managerial implications for enterprises and government are offered.

The power industry is the industry with the most direct uses of fossil fuels in China and is one of China’s main carbon industries. A comprehensive and accurate analysis of the impacts of carbon emissions by the power industry can reveal the potential for carbon emissions reductions in the power industry to achieve China’s emissions reduction targets. The main contribution of this paper is the use of a Generalized Divisia Index Model for the first time to factorize the change of carbon emissions in China’s power industry from 2000 to 2015, and gives full consideration to the influence of the economy, population, and energy consumption on the carbon emissions. At the same time, the Monte Carlo method is first used to predict the carbon emissions of the power industry from 2017 to 2030 under three different scenarios. The results show that the output scale is the most important factor leading to an increase in carbon emissions in China’s power industry from 2000 to 2015, followed by the energy consumption scale and population size. Energy intensity levels have always promoted carbon emissions reduction in the power industry, where energy intensity and carbon intensity effects of energy consumption have great potential to mitigate carbon levels. By setting the main factors affecting carbon emissions in the future three scenarios, this paper predicts the carbon emissions of China’s power industry from 2017 to 2030. Under the baseline scenario, the maximum probability range of the potential annual growth rate of carbon emissions by the power industry in China from 2017 to 2030 is 1.9–2.2%. Under the low carbon scenario and technological breakthrough scenario, carbon emissions in China’s power industry continue to decline from 2017 to 2030. The maximum probability range of the potential annual drop rate are measured at 1.6–2.1% and 1.9–2.4%, respectively. The results of this study show that China’s power industry still has great potential to reduce carbon emissions. In the future, the development of carbon emissions reduction in the power industry should focus on the innovation and development of energy saving and emissions reduction technology on the premise of further optimizing the energy structure and adhering to the low-carbon road.

Electricity production has a significant impact on the Water-Energy-Food nexus sectors as it requires substantial amounts of water and land, whilst also being a primary polluter of these resources. In addition, electricity production is a key contributor to global carbon emissions. With electricity production predicted to increase by over 50% by 2050, the impact of electricity production on water and land resources, as well as on carbon emissions, will need to be significantly reduced. This is particularly important in countries facing water, energy, and food scarcity and insecurity, such as South Africa. This paper therefore investigates the impact of electricity production on the Water-Energy-Food nexus sectors and on carbon emissions in South Africa. To do this, we conduct a lifecycle assessment of the water and land required for electricity production, as well as its carbon emissions in South Africa, by electricity source, and under key scenarios. The results from the Integrated Resource Plan 2030 scenario show that despite a 34% increase in electricity production targeted from 2018 to 2030 in South Africa, the water and land requirements, and the carbon emissions of electricity production would decrease by 29%, 9%, and 5.5%, respectively. Compared to the Business-as-Usual 2030 scenario, it was shown that the water and land requirements and carbon emissions of electricity production would be 55.5%, 42.6%, and 41.5% lower in the Integrated Resource Plan 2030 scenario, respectively. Overall, the results show that to reduce the impact of electricity production on the Water-Energy-Food nexus sectors and on carbon emissions, switching away from fossil fuels (particularly coal), and promoting the use of non-hydro and non-biomass renewables is required.