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The paper by Kiss and Szalay ([12]) titled I Sensitivity of buildings' carbon footprint to electricity decarbonization: a life cycle-based multi-objective optimization approach i examines how changes in electricity mix impacts GHG emissions. The Intergovernmental Panel on Climate Change (IPCC) emphasizes the need to immediately tackle climate change by significantly reducing greenhouse gas (GHG) emissions and capping global warming at 1.5 °C above pre-industrial levels (IPCC, [8]). The study found that total GHG emissions from maize production were on average 429 ± 27 kg CO2-eq/ton grain, with the highest emissions occurring during the dry season. This SI further contributes to raising awareness to the topic of measuring GHG emissions, decarbonization measures as well as climate neutrality. [Extracted from the article]

An effective and just industrial transition is necessary both to mitigate climate change and protect jobs, and as a precursor to enable other sectors to decarbonise. Activity is at an early stage and examples of successful sector-wide interventions to decarbonise industry do not yet exist. Governments of industrialised countries are beginning to develop policy and provide funding to support deployment of carbon capture and low-carbon hydrogen infrastructures into high-emitting industrial clusters, but options for sites outside of clusters, denoted here as 'dispersed sites', are also required. This paper takes a mixed methods approach to provide the first analysis of the issues facing dispersed industrial sites on their route to decarbonisation and to suggest solutions to the challenges they face. Using the UK as a case study, it first characterises dispersed sites in terms of location, emissions released, sectors involved, and size of companies affected. It then shows how these features mean that simply expanding the geographical scope of the present UK decarbonisation strategy, which focuses on the provision of carbon capture and low-carbon hydrogen, would face a number of challenges and so will need to be broadened to include a wider range of abatement options and other considerations to meet the needs of dispersed sites. While the solutions for each place will be different, these are likely to include some combination of the expansion of shared infrastructure, the development of local zero-carbon hubs, research into a wider range of novel abatement technologies and facilitating local participation in energy planning. The paper concludes with a discussion of remaining knowledge gaps before outlining how its findings might apply to industrial decarbonisation strategies in other countries. [ABSTRACT FROM AUTHOR]

Energy is known as the lifeline of national development, and from the last decade or so, the security of human energy supply has been frequently disturbed by natural disasters due to global warming and frequent geological activities. In this paper, by combing the literature, we found that the past literature on the relationship between energy and natural disasters mainly focused on measuring energy consumption under natural disasters, but less on the integrated evaluation of human economic activities, energy security and natural disasters, and less on the evaluation of the energy governance efficiency of each provincial, municipal, and autonomous region government in preventing and managing exposure to natural disasters from the perspective of Chinese provinces. Therefore, in order to fill the gap in the literature in this field, this paper collects panel data of energy consumption efficiency stages and natural disaster treatment stages from 2013 to 2017 for 30 provinces in China (excluding Hong Kong, Macao and Taiwan as well as Tibet Autonomous Region) and uses the two-stage undesirable dynamic DDF model as a framework to study the relationship between economic, environmental pollution and natural disasters for the sample data and to analyze the phase-by-phase evaluation of energy and natural disaster efficiency and make corresponding policy recommendations. The following conclusions are drawn: (1) The overall efficiency of China's eastern coastal provinces is higher than that of the central and western provinces. (2) The first-stage efficiency of Chinese provinces is better than the second stage, and the difference in efficiency of the first stage of each province is smaller than that of the second stage. (3) In terms of the efficiency of disaster prevention and relief inputs, the efficiency values are generally lower in most regions of China. (4) Finally, the annual efficiency of natural disaster losses is not high in all regions, and the efficiency values are higher in the eastern coastal regions than in the central and western regions. Accordingly, this paper proposes that each province should formulate relevant disaster prevention and economic development strategies according to regional characteristics, while the central government should also propose locally appropriate coordinated governance policies to effectively control carbon dioxide emissions and air pollution, as well as increase disaster prevention publicity and incorporate disaster prevention education-related work into the performance assessment mechanism of local governments to promote the interactive development of the two. [ABSTRACT FROM AUTHOR]

Despite the negative externalities on the environment and human health, today's economies still produce excessive carbon dioxide emissions. As a result, governments are trying to shift production and consumption to more sustainable models that reduce the environmental impact of carbon dioxide emissions. The European Union, in particular, has implemented an innovative policy to reduce carbon dioxide emissions by creating a market for emission rights, the emissions trading system. The objective of this paper is to perform a counterfactual analysis to measure the impact of the emissions trading system on the reduction of carbon dioxide emissions. For this purpose, a recently-developed statistical machine learning method called matrix completion with fixed effects estimation is used and compared to traditional econometric techniques. We apply matrix completion with fixed effects estimation to the prediction of missing counterfactual entries of a carbon dioxide emissions matrix whose elements (indexed row-wise by country and column-wise by year) represent emissions without the emissions trading system for country-year pairs. The results obtained, confirmed by robust diagnostic tests, show a significant effect of the emissions trading system on the reduction of carbon dioxide emissions: the majority of European Union countries included in our analysis reduced their total carbon dioxide emissions (associated with selected industries) by about 15.4% during the emissions trading system treatment period 2005–2020, compared to the total carbon dioxide emissions (associated with the same industries) that would have been achieved in the absence of the emissions trading system policy. Finally, several managerial/practical implications of the study are discussed, together with its possible extensions. [ABSTRACT FROM AUTHOR]

The goals of this paper are to examine whether the environmental Kuznets curve (EKC) holds and to investigate whether renewable energy consumption can decrease CO2 emissions in the USA using monthly data spanning the period 2000:M01–2018:M07. For these purposes, the paper employs a cointegration test with a regime shift and observes the long-run coefficients before and after the regime shift. The findings support the presence of the EKC. The findings also indicate that renewable energy consumption has negative effects on CO2 emissions, while these effects are greater when the share of renewable energy consumption in total energy consumption is higher in the USA. Theoretical and practical implications for these findings are discussed. [ABSTRACT FROM AUTHOR]

Scientific analysis of regional agricultural carbon emission prediction models and empirical studies are of great practical significance to the realization of low-carbon agriculture, which can help revitalize and build up ecological and beautiful countryside in China. This paper takes agriculture in Guangdong Province, China, as the research object, and uses the extended STIPAT model to construct an indicator system for the factors influencing agricultural carbon emissions in Guangdong. Based on this system, a combined Isomap–ACO–ET prediction model combing the isometric mapping algorithm (Isomap), ant colony algorithm (ACO) and extreme random tree algorithm (ET) was used to predict agriculture carbon emissions in Guangdong Province under five scenarios. Effective predictions can be made for agricultural carbon emissions in Guangdong Province, which are expected to fluctuate between 11,142,200 tons and 11,386,000 tons in 2030. And compared with other machine learning and neural network models, the Isomap–ACO–ET model has a better prediction performance with an MSE of 0.00018 and an accuracy of 98.7%. To develop low-carbon agriculture in Guangdong Province, we should improve farming methods, reduce the intensity of agrochemical application, strengthen the development and promotion of agricultural energy-saving and emission reduction technologies and low-carbon energy sources, reduce the intensity of carbon emissions from agricultural energy consumption, optimize the agricultural planting structure, and develop green agricultural products and agro-ecological tourism according to local conditions. This will promote the development of agriculture in Guangdong Province in a green and sustainable direction. [ABSTRACT FROM AUTHOR]

Corporate social responsibility (CSR) is becoming increasingly important in the field of corporate sustainability. However, little literature has focused on the relationship between CSR and corporate carbon emissions in developing countries. This paper aims to fill this gap by exploring the relationship between CSR and corporate carbon intensity from the perspective of financing constraints. We examine the mediating effects of financing constraints using a mediating effects model by using Chinese listed companies data from 2011 to 2019. The analysis results of this paper are as follows: (1) CSR can reduce corporate carbon intensity. (2) Financing constraints have a positive mediating role between the two. Namely, CSR can reduce the financing difficulties of enterprises, and enhance carbon–neutral capacity. (3) The carbon emission reduction effect of CSR is different in different life cycles. (4) CSR has a greater impact on the carbon intensity of state-owned enterprises, high-tech enterprises, and heavy polluting enterprises. These facts provide meaningful references for developing countries such as China to promote CSR and carbon governance. [ABSTRACT FROM AUTHOR]

Even though remittance inflow in Bangladesh is a significant earning source for recipient individuals and one of the vital factors to the development model, its environmental impacts have been ignored in the empirical investigation. To fill this research gap, this article aims to elucidate the asymmetric effectiveness of remittance inflow on environmental degradation in Bangladesh by addressing foreign aid as an additional multiplier in a carbon dioxide (CO2) emissions model, a first for this country. This paper, in doing so, utilizes the newly advanced nonlinear autoregressive distributive lag (NARDL) approach. Data examined are yearly observations throughout 1980–2016, and empirically, the results of the NARDL bound test confirm a long-run cointegrated causality between remittance and CO2 emissions via approving the theoretical linkage. The asymmetric findings interpret that the intensity of CO2 emissions raises in the long run relative to the positive shock in remittance, while the elasticity of CO2 emissions is significant and negative due to the negative shock in remittance. The estimation also reveals the presence of a long-run asymmetric association between the variables. In addition, foreign aid is favorable to ameliorate the quality of the environment over a long period. Focusing on the findings, this paper recommends that the government should concentrate on environment favorable projects and also take measures of tax incentives to remittance investors. [ABSTRACT FROM AUTHOR]

Current studies do not provide a consensus on whether digital technology innovation can reduce enterprise carbon intensity despite the rise of the digital economy. This paper examines the role and influence pathway of digital technology innovation on enterprise carbon intensity using data from A-share listed enterprises in China's manufacturing industry from 2012 to 2021. The findings indicate that (1) digital technology innovation has been found to significantly reduce enterprise carbon intensity, as confirmed by numerous robustness and endogeneity tests. However, its inhibitory effect on carbon intensity shows a marginal decreasing trend. (2) In the heterogeneity analysis, it was found that digital technology innovation significantly reduces the carbon intensity of consuming coal, coke, kerosene, and diesel. From various perspectives, including enterprise, industry, and external environment, there are significant differences in the carbon reduction effects of digital technology innovation. (3) The analysis of impact paths reveals that digital technology innovation can affect enterprise carbon intensity through three paths: improving productivity, enhancing green innovation efficiency, and adjusting energy consumption. (4) Upon further analysis, it was discovered that the spillover effect of digital technology innovation is more pronounced in the industry cohort of enterprises. Additionally, digital technology innovation plays a positive role in enhancing enterprise ESG performance. The paper's findings offer empirical evidence and decision-making references for the government to develop reasonable policies for reducing carbon emissions, promoting green and low-carbon enterprise transformation, and actively and steadily achieving the goal of carbon peaking and carbon neutrality. [ABSTRACT FROM AUTHOR]

The decarbonization of the shipping industry is a critical imperative in the global fight against climate change. Non-EU countries, being significant contributors to shipping emissions, play a crucial role in shaping the industry's sustainable future. However, securing funding for shipping decarbonization initiatives in these countries presents challenges, such as limited access to capital, lack of financial initiatives, political and regulatory uncertainties, technological risks, lack of local expertise, and the effects of global economic volatility. Addressing these challenges demands innovative strategies. The paper explores ways of effectively allocating funds for decarbonization projects in the shipping industry of non-EU countries, Türkiye in particular, using a structured decision-making framework tailored to their specific needs and challenges. We adopt the Moment Integrated Solution Method (THEMIS) to identify the "best" option from the range of alternative strategies identified in the framework. Our findings show that the use of incentive mechanisms is the most prioritized funding alternative, followed by the implementation of a sound Cap-and-Trade system and the promotion of strict local regulations to combat emissions. These imply that the prioritization of funding mechanisms, market-driven approaches, and more stringent regulation are key drivers in maritime decarbonization efforts. The Türkiye case study on strategies to achieve a greener maritime industry in developing countries also shows that securing funding for decarbonization requires a concerted effort by governments, private entities, and international organizations. [ABSTRACT FROM AUTHOR]

As an emerging technology, industrial intelligence focus on the integration of artificial intelligence and production, which creates a new access to achieve the goal of carbon emissions reduction. Using data on provincial panel data from 2006 to 2019 in China, we empirically analyze the impact and spatial effects of industrial intelligence on industrial carbon intensity from multiple dimensions. Results show an inverse proportionality between industrial intelligence and industrial carbon intensity, and the mechanism is to promote green technology innovation. Our results remain robust after accounting for endogenous issues. Viewed from spatial effect, industrial intelligence can inhibit not only the industrial carbon intensity of the region but also the surrounding areas. More strikingly, the impact of industrial intelligence in the eastern region is more obvious than that in the central and western regions. This paper effectively complements the research on the influencing factors of industrial carbon intensity and provides a reliable empirical basis for industrial intelligence to reduce industrial carbon intensity, as well as a policy reference for the green development of the industrial sector. [ABSTRACT FROM AUTHOR]

Purpose: Life cycle assessment (LCA) is a widely used method for the evaluation of buildings' environmental impacts, but these analyses contain high levels of uncertainty. Decarbonization of electricity production is key to reach climate goals, influencing all sectors including construction The objective of this paper is to study the sensitivity of the environmentally optimum building design solution to a changing electricity mix to assist decision-making. Methods: In this paper, multi-objective optimization was applied to minimize the life cycle global warming potential and life cycle costs of a building at the same time, using dynamic energy simulation and LCA. The variables include building envelope parameters such as window ratio, insulation type and thickness in a typical new multi-family apartment building heated with a heat pump. A static, largely fossil-based electricity mix and a dynamic, gradually decarbonizing alternative are considered, as well as two electricity price increase scenarios. New metrics have been introduced to explore the results and describe the Pareto-optimal solutions, for example the improvement potential to contextualize the achievements through the optimization. Results and discussion: The results show that, with the current electricity mix, building envelope optimization can improve the design by 18% on average in terms of the life cycle greenhouse gas emissions compared to typical new designs and by 10% with a dynamic mix in Hungary. With today's static electricity mix, the optimization proved that the minimum energy efficiency requirements in force are close to cost optimality. However, from an environmental point of view, much higher insulation thicknesses have been shown as justified (U-values of less than 0.1 W/m2K). Conclusions: Optimization of building design based on LCA is still not widely applied and the effect of a decarbonizing electricity mix on the optimum has not been studied before. The results of the paper demonstrate the importance of this question. Optimal solutions are different depending on the applied mix but an energy-efficient building envelope remains important: today's cost-optimal building envelope proves to be both cost- and environmentally optimal in the long run. [ABSTRACT FROM AUTHOR]

The collaborative agglomeration of manufacturing and producer services is an essential tool for the green transformation of China's economic model. This paper explores the impact of industrial collaborative agglomeration on carbon intensity, using the spatial Durbin model (SDM) based on China's provincial panel data from 2012 to 2019. The empirical results indicate that there is an inverted N-shaped relationship between industrial collaborative agglomeration and carbon intensity, with the turning points of 2.5255 and 2.8575. Regional industrial collaborative agglomeration tends to initially reduce carbon intensity, then aggravates to carbon emission, then finally inhibits carbon intensity. There is an obvious heterogeneity in the impact of producer-service subsectors and manufacturing collaborative agglomeration on carbon intensity. When the industrial collaborative agglomeration level exceeds a certain threshold, the clustering of information transmission, software and information technology service, and financial intermediation service have the greatest emission reduction potential. Industrial collaborative agglomeration has obvious spatial spillover effect, and carbon intensity has obvious spatial convergence effect. This paper provides some novelties for research perspectives on carbon intensity reduction and theoretical references for the development and implementation of differentiated industrial collaborative agglomeration policies. [ABSTRACT FROM AUTHOR]

The free flow of factors resulting from regional integration affects not only economic and social activities between cities, but also climate and environmental changes. Under the goal of sustainable development, regional integration as a policy pathway to lower carbon intensity has rarely been clarified. In light of this, using panel data of 279 prefecture-level cities in China from 2006 to 2019, this paper treats city cluster planning as a quasi-natural experiment of regional integration and aims to discuss the impact and mechanism of regional integration on carbon intensity through difference-in-differences (DID) method, mediating effect model and moderating effect model. The following results are reached: (1) The implementation of regional integration policy effectively reduces carbon intensity, and the conclusion still holds after a series of robustness tests. (2) Total factor productivity (TFP) is the mediator of regional integration to reduce carbon intensity, and population density, technological innovation and industrial upgrading are the moderators. (3) The effect of regional integration in reducing carbon intensity varies from city location and city features. The proposed theoretical framework of regional integration affecting carbon intensity, containing mediator and moderators, not only enriches the theoretical research in the field of environment and region, but also provides a new policy path for exploring sustainable development. Therefore, it is necessary to build urban agglomerations integrating the concepts of green, low carbon, intensive and efficient. [ABSTRACT FROM AUTHOR]

Wind power is a key for decarbonizing economies. Yet, wind turbines can produce negative environmental externalities. These include bird collisions and disamenities for residents. Setback distances for onshore wind turbines to settlements and bird nests are a common policy instrument to address these externalities. In this paper, we evaluate the cost-effectiveness of setback distances. We use a spatially-explicit model to investigate the social costs of different options for setbacks to settlements and red kite nests. In terms of social costs, we consider production costs and external costs associated with residents' disamenities and red kite impacts given an exogenously set wind energy production target. The model is applied using data for the German State of Saxony. Three key results are: (1) More restrictive setbacks may increase social costs. (2) Uniform red kite setbacks reduce social costs rather than uniform settlement setbacks. (3) Differentiated settlement setbacks for different settlement types lead to substantially lower social costs than uniform setbacks. [ABSTRACT FROM AUTHOR]

Decarbonization of economies is seen as a high priority for many countries to reduce the impact of climate change. As such, policy makers need robust and reliable decoupling indicators to assess decarbonization progress. This paper utilizes a regression-based approach to estimate decoupling indicators and identify decoupling states in the transport sector. The proposed method is easy to use, data driven, providing robust and reliable estimates of decoupling elasticities and fits within existing policy frameworks. Analyzing the transport sector for 59 countries from across the world beginning 1970, only 16 are weakly decoupling. Most of these have been identified as being part of a group with sustained economy-wide CO2 reductions. Stronger and localized green transport policy action is needed to meet emissions targets for most countries. [ABSTRACT FROM AUTHOR]

China faces tough challenges in the process of low-carbon transformation. To determine whether China can achieve its new 2030 carbon peaking and carbon intensity reduction commitments, accurate prediction of China's CO2 emissions is vital. In this paper, the random forest (RF) model was used to screen 26 carbon emission influencing factors, and seven indicators were selected as key variables for prediction. Subsequently, a three-layer back propagation (BP) neural network was constructed to forecast China's CO2 emissions and intensity from 2020 to 2040 under the 13th Five-Year Plan, 14th Five-Year Plan, energy optimization, technology breakthrough, and dual control scenarios. The results showed that energy structure factors have the most significant impact on China's CO2 emissions, followed by technology level, and economic development factors are no longer the main drivers. Under the 14th Five-Year Plan scenario, China can achieve its carbon peaking on time, reaching 10,434.082 Mt CO2 emissions in 2030. Although the new commitment to intensity reduction (over 65%) under this scenario cannot be achieved, the 14th Five-Year Plan can bring about 73.359 and 539.710 Mt of CO2 reduction in 2030 and 2040 respectively, compared to the 13th Five-Year Plan. Under the technology breakthrough and dual control scenarios, China will meet its new commitments ahead of schedule, with the dual control scenario being the optimal pathway for CO2 emissions to peak at 9860.08 Mt in 2025. It is necessary for Chinese policy makers to adjust their current strategic planning, such as accelerating the transformation of energy structure and increasing investment in R&D to achieve breakthroughs in green technologies. [ABSTRACT FROM AUTHOR]

Background: Decarbonisation of the European economy is one of the main strategic goals of energy transition in the European Union (EU), which aims to become a leader in this process by 2050 and to include other European countries making thus the European continent the first carbon neutral region in the world. Although decarbonisation is an important goal of the EU, the models for monitoring the progress of this process have not yet been clearly defined, and views on the social, economic, and security implications in terms of prioritising decarbonisation are conflicting. The main objective of this paper is to determine the methodological correctness of the existing method of decarbonisation monitoring, to develop a new monitoring model indicating the differences in the EU and European countries that are non-EU and to point out the underlying social, economic and security implications that must certainly find their place in the decision-making process in this field. Results: The main results showed that there is no clearly defined model for monitoring the success of decarbonisation, while the indicators that are commonly used for this purpose make a model that, as the analysis shows—is methodologically incorrect. In the case of EU countries, the following indicators proved to be the most reliable: consumption-based CO2 and share in global CO2. For non-EU countries, the best monitoring indicators are CO2 per unit of GDP, consumption-based CO2, and renewable energy consumption. These indicators can explain 99% of the variance in decarbonisation success. Conclusions: The basic conclusion of the paper is that even before the implementation, the decarbonisation monitoring model should be defined and methodologically tested, and the use of a single model for all EU countries or for all countries is not recommended. It is proposed to simplify the monitoring model, with an emphasis on monitoring of consumption-based CO2, which proved to be the most efficient in all sampled countries. The current method of monitoring is based exclusively on environmentally related indicators while ignoring the fact that decarbonisation is associated with almost all aspects of development. The additional social, economic and security aspects need to be developed and included in the further monitoring process. [ABSTRACT FROM AUTHOR]

The world is facing the challenges of climate change and energy structure adjustments. The role of digital finance, a new branch of business that combines digital technology and traditional financial products, in reducing global carbon emissions needs to be studied. This paper uses panel data on 280 cities in China from 2011 to 2019 to empirically examine the efficacy of digital finance for governing carbon emission reductions and the mechanism by which it does so. The results show that (1) digital finance can facilitate carbon emission reductions and help reduce carbon emission intensity within regions; (2) digital finance helps promote the rational allocation of resources and alleviates factor distortions by encouraging firms to rationally use their own factor endowments so as to reduce carbon emission intensity, which holds robustly after considering the endogenous issues such as possibly omitting variables and collinearity; and (3) differences in geographical location, the vitality of regional innovation and entrepreneurship, regional willingness to protect the environment, and environmental protection levels lead to heterogeneity in the effect of digital finance on carbon emission intensity. Therefore, it is necessary to vigorously develop digital finance as a long-term tool for carbon governance. [ABSTRACT FROM AUTHOR]

Energy storage technology is the key to achieving a carbon emission policy. The purpose of the paper is to improve the overall performance of the combined cooling, heating and power-ground source heat pump (CCHP-GSHP) system by the battery. A new operation strategy (the two-point operation) is proposed by controlling the power generation unit work. The power generation unit has two operation modes of non-operation and rated efficiency operation by the storage electricity battery. The new operation strategy is compared with the traditional CCHP-GSHP that without a battery. The optimization goals include the primary energy saving ratio, the reduction ratio of carbon dioxide emissions, and the annual total cost saving ratio. The independent GSHP system is used as a reference system. Multipopulation genetic algorithms are selected to achieve the problem of optimization. A hotel building is selected for a case study. The optimal configuration of the coupling system is computed following the electric load strategy. Finally, the results show that the CCHP-GSHP system has a better performance under the new operation strategy compared with the traditional CCHP-GSHP (the primary energy saving ratio increases by 5.5%; the annual carbon dioxide emission reduction ratio increases by 1%; the annual total cost reduction ratio increases by 5.1%). This paper provides reference and suggestions for the integration and operation strategy of CCHP-GSHP in the future. [ABSTRACT FROM AUTHOR]

Background: The implementation of sustainability at universities means that they can also play a key role in the transition to a low carbon economy, and in assisting global efforts towards decarbonisation. Yet, not all of them have so far fully engaged in this area. This paper reviews the state of the art on trends in decarbonisation, and outlines the need for decarbonisation efforts at universities. It also reports on a survey aimed at ascertaining the extent to which universities in a sample of 40 countries across the various geographical regions are engaged in carbon reduction efforts, identifying the challenges faced. Results: The study has shown that the literature on the topic has been evolving over time and that increasing a given university's energy supply from renewable energy sources has been the cornerstone of university-based climate action plans. The study also indicates that even though several universities are concerned with their carbon footprint and actively seeking ways to reduce it, there are some institutional obstacles that need to be overcome. Conclusions: A first conclusion which can be drawn is that efforts on decarbonisation are becoming more popular, with a special focus being placed on the use of renewable energy. Also, the study has shown that, from the range of efforts being made towards decarbonisation, many universities are setting up a team with carbon management responsibilities, have Carbon Management Policy Statements, and review them. The paper points towards some measures which may be deployed, so as to allow universities to take better advantage of the many opportunities an engagement in decarbonisation initiatives offers to them. [ABSTRACT FROM AUTHOR]

In order to promote the balanced development of regional economy, governments at all levels are constantly introducing regional coordinated development policy (hereinafter referred to as "the Policy"). However, there is an important and interesting issue, namely, with the increasingly severe environmental problems resulted from rapid regional economic growth, what kind of impact will the Policy have on carbon emissions reduction? This is attracting wide attention from relevant stakeholders. Therefore, taking the Beijing–Tianjin–Hebei (BTH) region in China for example and through constructing the difference-in-differences (DID) model, this paper evaluated the effect of the implementation of the Policy on carbon emissions reduction. Results indicated that the Policy significantly reduced the level of regional carbon emissions in the BTH region. After carrying out a series of robustness tests, this paper still found that the above conclusions were reliable. Moreover, the mediation effect test shown that the Policy indirectly lessened carbon emissions by optimizing energy structure and reducing the intensity of carbon emissions, while the expanding of economic scale would lead to an increase in carbon emissions due to the effect from the Policy. Additionally, heterogeneity analysis revealed that the Policy had a more significant effect on carbon emissions reduction in underdeveloped regions with low environmental constraints. Overall, this paper would be beneficial to understanding the environmental effects of the Policy at the urban regional scale, thus providing an important basic theoretical basis for promoting the green and sustainable development of regional economy. [ABSTRACT FROM AUTHOR]

The carbon quota allocation rules of China's pilot carbon emissions trading (CET) regions are various, which mainly include benchmarking, historical carbon intensity reduction and auctioning. When the allocation rules change, it is unresolved how to achieve the optimal product prices and effectively allocate the carbon emissions reduction profits of CET-covered enterprises in the cooperative supply chain. Thus, this paper uses the Stackelberg game, Nash equilibrium and the Shapley value based on cost modification to investigate these issues. The results indicate that: (1) The increasing carbon prices can always improve the retail prices only under the auctioning rule. Meanwhile, the growing low-carbon awareness of consumer cannot be always conducive to improving the wholesale and retail prices, and the similar product prices of non-CET-covered enterprises have greater impact on the wholesale prices than that on the retail prices. (2) Only under the free carbon quota allocation rules, can the optimal wholesale and retail prices under the Stackelberg game be always higher than those under the Nash equilibrium. Meanwhile, the auctioning rule can better reduce carbon emissions than the free allocation rules. (3) Improving carbon emissions reduction contribution and emission reduction costs can be conducive to increasing the carbon emission reduction profits of the supplier and retailer, while the impact of carbon emission reduction contribution on improving the carbon emission reduction profits is not always greater than that of the carbon emission reduction costs. [ABSTRACT FROM AUTHOR]

The calculation of marginal abatement costs of CO2 plays a vital role in meeting China’s 2020 emission reduction targets by providing reference for determining carbon tax and carbon trading pricing. However, most existing researches only used one method to discuss regional and industrial marginal abatement costs, and almost no studies predicted future marginal abatement costs from the perspective of CO2 emission efficiency. To make up for the gaps, this paper first estimates marginal abatement costs of CO2 in three major industries of 30 provinces in China from 2005 to 2015 based on three assumptions. Second, based on the principle of fairness and efficiency, China’s 2020 emission reduction targets are decomposed by province. Based on the ZSG-C-DDF model, the marginal abatement costs of CO2 in all provinces in China in 2020 are estimated and compared with the marginal abatement costs of 2005 to 2015. The results show that (1) from 2005 to 2015, marginal abatement costs of CO2 in all provinces show a fluctuating upward trend; (2) compared with the marginal abatement costs of primary industry or tertiary industry, most provinces have lower marginal abatement costs for secondary industry; and (3) the average marginal abatement costs of CO2 for China in 2020 are 2766.882 Yuan/tonne for the 40% carbon intensity reduction target and 3334.836 Yuan/tonne for the 45% target, showing that the higher the emission reduction target, the higher the marginal abatement costs of CO2. (4) Overall, the average marginal abatement costs of CO2 in China by 2020 are higher than those in 2005-2015. The empirical analysis in this paper can provide multiple references for environmental policy makers. [ABSTRACT FROM AUTHOR]

The sustainable development of human beings is facing severe challenges in the context of a new era. The effective reduction of carbon emission intensity is essential to achieve the goal of sustainable development. Obviously, green innovation is an important factor in mitigating carbon emission intensity. It is important to measure the effect of green innovation on carbon emission intensity for accelerating industrial transformation and building a circular economy system. Therefore, this paper uses the data of 30 provinces in China from 2000 to 2019, obtained by the State Intellectual Property Office, using fixed effects model quantile regression model and Spatial Durbin Model to empirically verify the theoretical hypothesis. The conclusions are as follows: (1) Green innovation inhibits carbon emission intensity. Instrumental variable model and robustness test support this conclusion. (2) For carbon emission intensity under different quantiles, green innovation has a more significant effect on provinces with high carbon emission intensity through "target accountability system" and "reverse coercive system." (3) There is a significant spatial correlation between green innovation in China's provinces. The reduction of carbon emission intensity in the region will benefit from the improvement of green innovation in surrounding cities. [ABSTRACT FROM AUTHOR]

Dryers are utilized in food industry and agriculture in order to extend the useful lifespan of corps. Thermal energy is required for water removal in the process of drying which can be provided by different sources. Solar thermal energy is one of the most applicable sources for drying processes with several benefits such as avoidance of greenhouse gas emission and availability. Regarding the involvement of various factors in the performance of solar dryers, this paper focuses on the works conducted on these systems. In this regard, various types of solar dryers including direct, indirect, mixed-mode and hybrid supplied by solar energy are discussed. According to the outcomes of this review paper, it can be determined that the performance of the solar dryers depends on various parameters such as the type of dryer, solar irradiation, drying time and operating condition. Moreover, it is found that there are several approaches applicable for improving the overall performance of the solar dryers such as utilizing thermal energy storage units, applying solar tracker and using modified materials. Furthermore, there is high potential for integrating the solar dryers with other systems to achieve higher efficiency and reliability. In addition to energy analysis and drying capacity, the solar dryers have been investigated more deeply by applying exergy analysis and the dependency of exergy efficiency on the operating factors are discussed. Environmental analysis conducted on solar dryers reveals high potential of these systems in carbon dioxide mitigation. In addition, the determined payback periods of these systems are acceptable in majority of the investigated cases which shows their advantage in term of economy. [ABSTRACT FROM AUTHOR]

The increasing carbon emissions have been a major concern for most countries around the world. And as a result, every country is concerned about developing appropriate strategies to curtail it. As a major economy and the largest carbon emitter in the world, China has pledged to reduce the carbon intensity by 60–65% by 2030, compared with 2005 levels, and achieve carbon neutrality before 2060. Therefore, the analysis of the impact of China's carbon intensity is becoming an increasing important topic. Due to the spatial heterogeneity of carbon intensity, various spatial econometric models have been applied in this field. However, the existing literatures failed to consider the cross-products of relevant factors. This paper constructs our dynamic general nesting spatial panel model (GNS) with common factors to deal with the dilemma, and examines the direct and spatial–temporal spillover effects of industrial structure, GDP per capita, investment in anti-pollution projects as percentage of GDP and energy price on carbon intensity in China over the period 2003–2017. Our analysis shows that: (1) China's carbon intensity showed the spatial agglomeration and temporal "inertia" from 2003 to 2017. (2) From the time dimension, the long-term effect of industrial structure first increased and then gradually decreased. (3) From the spatial dimension, industrial structure and investment in anti-pollution projects as percentage of GDP accounted for the main spatial heterogeneity. Furthermore, this paper attempts to provide policy implications to help reduce carbon intensity and achieve carbon neutrality in China. [ABSTRACT FROM AUTHOR]

The development of renewable energy has become an important means for the world to cope with climate change, ensure energy security, and protect the ecological environment. Using the panel data of 30 provinces in China from 2013 to 2021, this study used the mediating effect model and the spatial Durbin model (SDM) to explore the mechanism and spatial effects of renewable energy development on China's regional carbon emission reduction. The results show that: (1) Renewable energy development can help to reduce carbon emission intensity. (2) The results of mechanism analysis show that renewable energy development reduces carbon intensity by improving energy structure, promoting industrial structure optimization, and industrial structure upgrading. (3) The development of renewable energy can not only reduce the local carbon intensity but also have a positive spillover effect on the carbon intensity of neighboring regions. (4) Further analysis shows that the long-term effect of renewable energy development on carbon emissions is greater than the short-term effect. At the same time, the heterogeneity analysis shows that compared with the Yellow River basin, the development of renewable energy has a significant carbon emission reduction effect in the Yangtze River Economic Belt region. Energy-rich areas fall into the "resource curse", which makes the carbon emission reduction effect of renewable energy development not significant. This paper has certain reference significance for promoting reasonable decomposition between regions and formulating renewable energy development policies. [ABSTRACT FROM AUTHOR]

In the energy transition, there is an urgent need for decreasing overall carbon emissions. Against this background, the purposeful and verifiable tracing of emissions in the energy system is a crucial key element for promoting the deep decarbonization towards a net zero emission economy with a market-based approach. Such an effective tracing system requires end-to-end information flows that link carbon sources and sinks while keeping end consumers' and businesses' sensitive data confidential. In this paper, we illustrate how non-fungible tokens with fractional ownership can help to enable such a system, and how zero-knowledge proofs can address the related privacy issues associated with the fine-granular recording of stakeholders' emission data. Thus, we contribute to designing a carbon emission tracing system that satisfies verifiability, distinguishability, fractional ownership, and privacy requirements. We implement a proof-of-concept for our approach and discuss its advantages compared to alternative centralized or decentralized architectures that have been proposed in the past. Based on a technical, data privacy, and economic analysis, we conclude that our approach is a more suitable technical backbone for end-to-end digital carbon emission tracing than previously suggested solutions. [ABSTRACT FROM AUTHOR]

To achieve carbon peak targets, realize carbon neutrality vision, and tackle global climate change, China must improve the carbon emission performance at the city level. Based on the carbon emission performance of 191 prefecture-level cities in China from 1997 to 2017, this paper analyses the evolution characteristics of urban carbon emission performance from three aspects: the overall spatial and temporal evolution, the differences according to both region and city size, and the differences among clusters categorized by carbon emission performance at the city level. This paper also reveals the impact of the social and economic transition on China's carbon emission performance. The results show that: (1) The overall level of carbon emission performance of Chinese cities is low, and there is a downward trend during the study period. The differences in carbon emission performance among cities are convergent, but there is a wide gap between high and low values. (2) The carbon emission performance of cities in eastern coastal areas is higher than that in non-coastal areas cities. Large urban agglomerations and economically developed regions, such as provincial capitals, are the agglomeration areas of high urban carbon emission performance values. (3) The carbon emission performance level of cities with similar sizes will converge. At the same time, such changes will enhance the differences among carbon emission performances at the city level within the same region. (4) Cities that belong to high urban carbon emission performance clusters are mainly distributed in the eastern region. Such cities are classified into large cities, supercities, and megacities. Compared with low urban carbon emission performance clusters, cities in high urban carbon emission performance clusters show a higher proportion in the medium-high level and high level of carbon emission performance. Moreover, cities in high urban carbon emission performance clusters are more likely to improve theirurban carbon emission performance. (5) The economic agglomeration effect, industrial structure adjustment and carbon intensity reduction have a significant impact on improving urban carbon emission performance. Population agglomeration has an incremental effect, and the anticipated benefits of environmental regulation have yet to be fully realized. The impacts of different clusters and different regions are variable. Finally, this paper advances policy enlightenment according to its research findings. [ABSTRACT FROM AUTHOR]

Commercial department assumes the vital part in energy conservation and carbon dioxide emission mitigation of China. This paper applies the time-series data covering 2001–2015 and introduces the STIRPAT method to research the factors of commercial department's carbon dioxide emissions in China. The combination of STIRPAT method and ridge regression is first adopted to research carbon dioxide emissions of commercial department in China. Potential influencing factors of carbon dioxide emission, including economic growth, level of urbanization, aggregate population, energy intensity, energy structure and foreign direct investment, are selected to establish the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) model, where ridge regression is adopted to eliminate multicollinearity. The estimation consequences show that all forces were positively related to carbon dioxide emissions in China's commercial department except for energy structure. Energy structure is the only negative factor and aggregate population is the maximal influencing factor of carbon dioxide emissions. The economic growth, urbanization level, energy intensity and foreign direct investment all positively contribute to carbon dioxide emissions of commercial department. The findings have significant implications for policy-makers to enact emission reduction policies in commercial sector. Therefore, the paper ought to take into full consideration these different impacts of above influencing factors to abate carbon dioxide emissions of commercial sector. [ABSTRACT FROM AUTHOR]

The substitution of fossil fuels, especially coal, with renewable energy is a crucial step for the CO2 emissions reduction and the avoidance of Global Climate Change. The electric power generation industry is the first economic sector that will have to transition to renewable energy. However, wind and solar energy, the two most abundant renewable energy forms, are not dispatchable. The high penetration of these renewables in the energy market will create a demand–supply mismatch, which can only be alleviated with large-scale energy storage. This paper uses the case of Texas—a state that generates and consumes more electricity than several large, industrialized nations—to quantitatively examine the required infrastructure for the decarbonization of the electricity generation industry, while satisfying the current electric power demand in the State. Among the parameters that are examined are: the additional solar and wind capacity; the necessary energy storage infrastructure; the energy dissipation in the storage/regeneration process; and the effect of decarbonization on the cost of electricity and the welfare of the citizens. The computations show that the technology is available for the transition to a decarbonized electric power sector but requires significant investment in new wind and photovoltaic units as well as substantial energy storage. This would increase the electricity prices by a factor between 2.9 and 3.7 and, would have a disproportionate impact on the citizens in the lower income brackets. [ABSTRACT FROM AUTHOR]

Based on the input–output table and considering the terms of import and export trade at the national level, this paper calculates China's annual direct carbon emissions and total carbon emissions from 2005 to 2015. On the one hand, the actual direct carbon emissions and actual complete carbon emissions at the national level are calculated by MRIO model; on the other hand, according to the 5-year emission reduction targets set in the Eleventh Five-Year Plan and the outline document of the Twelfth Five-Year Plan, the emission reduction targets for each year are determined by equal distribution over the years. Hence, the annual carbon intensity emission reduction targets for 2006–2010 and 2011–2015 can be determined to be 4% and 3.4%, respectively, to calculate the target direct carbon emissions and target goal for complete carbon emissions. The smaller of the two is taken as the total quota. Taking 2015 as an example, the total direct carbon quota and complete carbon quota calculated are 87.87 million tons and 71.85 million tons, respectively, which is the total amount to be allocated for inter-provincial direct carbon quota and inter-provincial complete carbon quota. Findings of the study hold pivotal implications for policy development. [ABSTRACT FROM AUTHOR]

The extensive use of natural gas provides a new approach to low carbonization of the structure of primary energy sources, as well as reduces the peak in carbon emissions before fossil fuels are completely replaced by clean energy. To explore the impact of the West–East Gas Pipeline Project (WEGPP) on energy conservation and emission reduction in Chinese inland areas, the effects of policy intervention on the socio-economic development in Hubei province are preliminary investigated in this paper. The causal inference of multi-bandwidth and multi-core analysis is carried out using regression discontinuity design (RDD), and the core conclusion of the counterfactual analysis is proven to be robust based on the synthetic control method. The results show that the transportation of natural gas from Western China to Hubei province can reduce the intensity of carbon emissions, demonstrating that it is an effective substitution to low-carbon energy. The WEGPP not only can meet the demands of a large number of energy shortages caused by the rise of the manufacturing industry, but also can solve the technique bottleneck of energy consumption in Central and Eastern China. The WEGPP has ensured the coordinated development in Western, Central, and Eastern China, achieving and maintaining a high and stable economic growth rate in China. These results have significant implications for making policies of energy conservation and emission reduction in a considerable number of gas-importing provinces in Central China. [ABSTRACT FROM AUTHOR]

This paper provides the evidence on the short- and the long-run effects of the export product concentration on the level of CO2 emissions in 19 developed (high-income) economies, spanning the period 1962-2010. To this end, the paper makes use of the nonlinear panel unit root and cointegration tests with multiple endogenous structural breaks. It also considers the mean group estimations, the autoregressive distributed lag model, and the panel quantile regression estimations. The findings illustrate that the environmental Kuznets curve (EKC) hypothesis is valid in the panel dataset of 19 developed economies. In addition, it documents that a higher level of the product concentration of exports leads to lower CO2 emissions. The results from the panel quantile regressions also indicate that the effect of the export product concentration upon the per capita CO2 emissions is relatively high at the higher quantiles. [ABSTRACT FROM AUTHOR]

How to reduce carbon emission intensity is a common challenge facing in various countries, particularly in developing countries. We identify three literature gaps: theoretical framework of a novel perspective of intelligent manufacturing (IM) influencing carbon intensity, empirical tests by solving endogeneity with cross country and industry data to answer why IM influences carbon intensity, and mediation analysis to explain how IM influences carbon intensity. By applying the World Input–Output Database (WIOD) and environmental account database with panel data of 13 manufacturing sectors in 39 economies from 2000 to 2011, we reveal that IM has a significant reduction in carbon intensity after considering endogeneity and robust check. Our mechanism test demonstrates that energy consumption structure and position in global value chains (GVCs) are main channels. Heterogeneity analysis reveals that developing countries have larger carbon restraining influence by IM. The contributions of this study are: First, this study investigates restraining influence on carbon intensity from novel perspective of IM, which enriches theoretical analysis. Second, this study takes advantage of cross country and industry data to test and adopts Generalized Method of Moments (GMM) model to solve endogeneity, which answers why IM influences carbon intensity. Further, this paper performs heterogeneity analysis and explores the mediating effects played by improving energy consumption structure and enhancing position in global value chains (GVCs), which answers how IM influences carbon intensity. [ABSTRACT FROM AUTHOR]

Carbon emissions are important factors causing global warming, which requires global efforts to deal with. In this paper, we investigate the mechanism of financial innovation on reducing carbon emissions in China by constructing a financial innovation development index with factors of green finance as well as fintech development. Empirical results show that financial innovation contributes to reduce carbon intensity by promoting energy structure transition as well as public fiscal expenditure on energy conservation and environmental protection. Moreover, heterogeneity exists in the effect of financial innovation on carbon emission reduction. Financial innovation has a significant role in reducing carbon intensity in eastern regions, but has a relatively small influence on central and western regions. Furthermore, financial innovation has a lag effect on reducing carbon intensity. [ABSTRACT FROM AUTHOR]

Agricultural carbon emission reduction is inseparable from discussions about government systems, especially in countries with distinctive institutional arrangements such as China. Environmental regulation may influence the impact of environmental decentralisation on agricultural carbon intensity. Using panel data from 30 provinces in mainland China from 2000 to 2015, this paper depicts the spatiotemporal pattern and dynamic evolution of environmental decentralisation, environmental regulation, and agricultural carbon intensity. It uses the spatial Durbin model to empirically study the internal connections between the three. The empirical results show that the enhancement of environmental decentralisation, environmental administrative decentralisation, environmental supervision decentralisation, and environmental monitoring decentralisation is not conducive to reducing agricultural carbon intensity. The results also show that the enhancement of the intensity of environmental regulation of "terminal governance type" and "front-end governance type" is conducive to reducing agricultural carbon intensity, but this effect has a negative "space spillover" phenomenon. Other findings are as follows: (1) the enhancement of the two types of environmental regulation intensity is conducive to changing the impact of environmental decentralisation on agricultural carbon intensity from a "grabbing-hand" to a "helping-hand"; (2) compared with balanced grain-producing and marketing areas and major grain-marketing areas, environmental decentralisation and two types of environmental regulations have a greater negative impact on the agricultural carbon intensity of major grain-producing areas. Therefore, the central government should change how it evaluates political performance; accelerate the construction of a diversified performance evaluation system; increase the weight of low-carbon, green, and other indicators that reflect the friendly development of agriculture in the system; and encourage local governments to adjust the direction of agricultural production. [ABSTRACT FROM AUTHOR]

This work investigates the decarbonization potential of large-scale implementation of BECCS (in the form of biomass co-firing and post-combustion amine-based CO2 capture) in over 150 existing coal-fired power plants in China, combining GIS-based geospatial analysis of biomass availability from agricultural residues and modelling of the techno-environmental performance of the retrofitted power plants. Using a 500-m resolution map of cropland in China, the availability of residual biomass at a radius of 25–100 km from each power plant is estimated. The biomass yield is then matched with the required input for a set of co-firing scenarios, identified by the optimal mix of different co-firing ratios (10%, 25%, and 40%) and increasing % of total plant's capacity to be retrofitted with the chosen BECCS system. Power generation performance and required biomass for the retrofitted plants are modelled in the Integrated Environmental Control Model (IECM). According to the results, the total decarbonization potential of the modelled large-scale implementation of BECCS ranges from 0.3 to 1.8 Gt CO2/yr (15–81% of BAU emissions), depending on the biomass collection radius. The Chinese provinces of Shandong, Jiangsu, Anhui, and Henan have the most potential for BECCS implementation, while coal-rich regions (e.g. Inner Mongolia) or highly developed coastal areas (Guangdong, Zhejiang) are constrained by local biomass availability. Overall, this work demonstrates the significant potential of the sustainable large-scale implementation of BECCS in China and provides suggestions to policymakers to include this technology in the portfolio of solutions towards carbon neutrality. [ABSTRACT FROM AUTHOR]

The increasing need for high-performance computing, preservation, and networking capabilities to support corporate and scientific applications is driving a rapid expansion in the use of cloud computing server farms. Virtual machine (VM) consolidation plays a crucial role in this context, involving the direct migration of VMs from underutilized physical servers to optimize power consumption efficiency, operational costs, and reduce CO2 emissions. A pivotal step in VM consolidation is the detection of host overload, which aims to predict potential server over-subscription with VMs. This paper introduces an Optimized Support Vector Regression model for overloaded detection. To enhance the Support Vector Regression (SVR) performance, optimal selection of SVR parameters is achieved using the Enhanced Tasmanian Devil Optimization algorithm. Following overload detection, VM migration occurs, but this process raises concerns about system integrity and data confidentiality. To address these concerns, data is encrypted using the Cyclic Shift Transposition Algorithm before migration. The proposed approach's performance is evaluated across various metrics such as energy consumption, ESV, Migration, and SLA X0.001, and its effectiveness is compared with different existing methods. [ABSTRACT FROM AUTHOR]

The digital economy and the pursuit of carbon peak and carbon neutrality have emerged as crucial focal points for China's future development. However, the intricate relationship between the digital economy and carbon intensity remains uncertain. Based on the construction of the digital economy evaluation index system, using panel data for 30 provinces in China from 2011 to 2019, this study estimates the impact of the digital economy development on carbon intensity by adopting the system-generalized method of moments (SYS-GMM) technique. The results show that the digital economy can effectively reduce the carbon intensity. This conclusion was supported by robustness tests. However, the carbon emission reduction effect of the digital economy exhibits heterogeneity with respect to the digital economy dimensions and regions. In addition to digital industrialization and industrial digitization reducing the carbon intensity, the digital economy development carrier has an inverted U-shaped nonlinear relationship with carbon intensity. Additionally, the digital economy has a more obvious inhibitory effect on carbon intensity in the eastern region. Most importantly, besides the mediating effects of technological progress and financial development, this paper finds that the digital economy can increase carbon intensity through human capital accumulation. These conclusions provide a certain scientific basis for the effective implementation of China's digital economy and carbon peak and carbon–neutral development strategy. [ABSTRACT FROM AUTHOR]

Green credit is an important green financial policy tool to promote green development. However, research is needed to explore how green credit reduces carbon emissions, especially with respect to its dynamic spatial interactions and regional disparities. Based on a theoretical analysis, this paper empirically tests the carbon emission reduction effect of green credit and its three mechanisms by combining a Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, dynamic spatial Durbin model (SDM), and the mediation model, including their dynamic spatial interactions and regional disparities. The study concludes that green credit can reduce carbon emission intensity based on strong spatio-temporal interactions in China. Green credit mainly reduces carbon emission intensity through scale and technology mechanisms with different spatio-temporal interactions. The tertiary industry in China does not currently have completely clean production; as such, the upgrading of the industrial structure as stimulated by green credit in the long term cannot yet effectively reduce carbon emissions. In addition, the carbon emission reduction effect of green credit and its three mechanisms have different levels of performance and dynamic spatial interactions in different regions of China. Finally, targeted policy recommendations are proposed to apply green credit to effectively reduce the carbon emission intensity. [ABSTRACT FROM AUTHOR]

The objective of this paper is to offer an in-depth description of Taiwan's success in green public procurement (GPP) efforts through legal systems and innovative measures, which were relevant to the contribution to the greenhouse gas emissions mitigation addressed in the United Nations Framework Convention on Climate Change (UNFCCC). To be in accordance with the certified green-mark products implementation since 2002, the benefit analysis was presented to connect with the trends of related sustainability indicators, including electricity consumption, carbon dioxide (CO) emissions, tap water consumption, municipal solid waste (MSW) and industrial waste (IW) recycling ratios, and renewable electricity purchase. At this period, the electricity consumption per capita slightly increased from 8.5 MWh in 2002 to 10.4 MWh in 2012. By contrast, CO emissions intensity remarkably decreased from 0.71 kg CO/US$ in 2002 to 0.53 kg CO/US$ in 2012. More consistently, the tap water consumption per capita dropped from 0.35 m/capita-day in 2001 to 0.27 m/capita-day in 2012. The resource recycling ratios (i.e., 40.40, and 82.40 %) from MSW and IW in 2011 have been relatively high as compared to those in 2002 (i.e., 15.55 and 72.67 %). Furthermore, the renewable electricity purchased by the Taipower (one of the nation-owned enterprises) remarkably increased from 1933.3 GWh in 2002 to 3183.9 GWh in 2012. It was positively connected with experiencing a significant progress towards green economy and CO emissions mitigation in Taiwan partly due to GPP from the green-mark products consumption and the renewable electricity purchase. Finally, some global adaptation and mitigation strategies for GPP and green-mark products were addressed in the paper. [ABSTRACT FROM AUTHOR]

To avoid an expected traffic jam, drivers make detours based on limited information; however, the majority following the alike routes may result in an unexpected congestion. Conventional navigation approaches are unable to respond to the unexpected congestion because these approaches do not consider the routes taken by other vehicles. Navigation systems that utilize global traffic information can improve gas consumption, CO emissions and travel time. Therefore, in this paper, the authors propose an autonomic navigation system (ANS) operating over vehicular ad-hoc networks (VANETs). The proposed ANS adopts a hierarchical algorithm to plan vehicle routes. The proposed ANS imitates the human nervous system when managing the navigation system, in which vehicles monitor traffic via VANETs. Moreover, this paper proposes a time-dependent routing algorithm that uses a novel traffic prediction method based on the routes of vehicles. This paper adopts EstiNet as simulator tool that dominates hundreds or thousands of VANET-based vehicles routing in two maps, Manhattan area, and Taipei city. The results show that the proposed ANS improves the average speed by 60.02 % when compared with the shortest path first (SPF) algorithm and by 15.49 % when compared with the distributed method of a traffic simulation in the Manhattan area. The proposed ANS also improves the average speed by 30.5 % when compared with the SPF algorithm and by 15.8 % when compared with the distributed method of a traffic simulation in the Taipei area. Furthermore, to emulate real environments, there is a scenario in which only a portion of the vehicles complies with the proposed ANS. [ABSTRACT FROM AUTHOR]

In 2017, large oil and gas companies formed the Climate Leadership Council (CLC) in response to increased social pressure to reduce their emissions to combat climate change. The CLC developed the Carbon Dividends Plan (CDP), which proposes a carbon emissions market mechanism to solve the climate change crisis. The CLC's lobbying arm, the Americans for Carbon Dividends (AFCD), is currently working to educate US congressional leaders on the importance of a carbon tax, like the one proposed in the CDP. In the paper, I address the following question: what can Polanyi's theoretical insights tell us about the CDP and its social implications? I first discuss the CDP within the context of the current climate change crisis. Then, I apply Polanyi's market embeddedness, fictitious commodities, and double movement concepts to the discussion. I illustrate four main points in this paper. First, the CDP is part of an ongoing societal double movement. Second, despite the CLC's neoliberal "free" market ideals, the self-regulated market proposed in the CDP is a social construction, embedded in social relations, itself. Third, the CDP, and carbon markets in general, comprise one specific type of unconscious countermovement that extends the self-regulated market to combat the destructive forces of the market itself. Fourth, the CDP's proposal for a carbon emissions market mechanism is an unsatisfactory solution to address the climate change crisis, as it will not address the inherent contradictions in the overall system as a whole. [ABSTRACT FROM AUTHOR]

This paper deals with data sharing among firms along a supply chain for the calculation of Scope 3 emissions. Scope 3 emissions are indirect emissions produced along a firm's supply chain. They represent the majority of most firms' carbon footprint. Current estimations of Scope 3 emissions are largely based on industry averages and other approximations, which leads to several disadvantages. By contrast, primary data sharing along the supply chain would provide firms with precise measures of Scope 3 emissions (that are eventually necessary for decarbonizing supply chain emissions beyond industry averages). For that, firms need access to data and information from their suppliers that are not under their control. We review the benefits of and three main obstacles to such data sharing: legal and regulatory challenges, missing interoperability, and data privacy concerns. Finally, we discuss initiatives and approaches for overcoming these obstacles. [ABSTRACT FROM AUTHOR]

Sea levels have been rising at an increasing rate in the past decades, due to the increased ocean temperatures and glacier melt caused by global warming. The continued increase in sea levels will result in large-scale impacts in coastal areas as they are submerged by the sea. Locations not able to bear the costs of implementing protection and adaptation measures will have to be abandoned, resulting in social, economic, and environmental losses. The most important mitigation goal for sea level rise is to reduce or possibly revert carbon dioxide (CO2) emissions. However, given the magnitude and long time lag between emissions and impacts, new adaptation measures to reduce sea level rise should be proposed, developed and if possible, implemented. This paper suggests that submerged barriers or dams built in front of ice sheets and glaciers would contribute to reducing the ice melt in Greenland. The ten proposed barriers or dams in this paper could prevent the contribution to sea level rise by up to 5.3 m at a cost of US$ 0.275 billion a year. This is much lower when compared to adaptation measures to sea level rise around the world estimated to be US$ 1.4 trillion a year by 2100. [ABSTRACT FROM AUTHOR]

To effectively combat global warming, an enormous reduction in CO2 emissions is required. Cameroon, which is currently the largest emitter of CO2 in the CEMAC subregion, has committed to reducing its greenhouse gas emissions by 32% by 2035. However, previous studies in Cameroon have only addressed the relationship between economic growth, energy consumption, and CO2 emissions without estimating all causal relationships at the same time. Moreover, no study has yet decomposed this country's CO2 emissions to date. To fill these research gaps and further assess the determinants of these CO2 emissions, an extended Kaya identity and the Logarithm Mean Divisia Index (LMDI I) have been applied in this paper to identify, quantify, and explain the main drivers of Cameroon's CO2 emissions from 2007 to 2014. Seven effects were measured and the main findings show that carbon intensity and the emission factor increased by 0.57% and 107.50% respectively. Regarding contributions to the increase of CO2 emissions, the population effect was the most positive followed by the activity effect, whereas the energy intensity, the substitution of fossil fuels and the penetration of renewable energies have contributed to reduce the CO2 emission. To enable Cameroon to not only achieve the goals of its vision but also develop a low-carbon economy, this paper provides some proposed avenues that should be considered by policymakers. [ABSTRACT FROM AUTHOR]

This paper uses the 1990-2010 natural disaster and carbon emission data of G20 countries to examine the impact of natural disasters and climate change on the natural capital component of inclusive wealth. Our study shows that climate change and GDP have no positive impacts on the growth of natural capital. By contrast, trade openness and natural disaster frequency contribute to the accumulation of natural capital in G20 countries. There is an inverted U-shaped relationship between the growth of natural capital and the magnitude of natural disaster. Natural capital growth is not affected very much by small disasters. By contrast, large disasters tend to make the growth of natural capital fall sharply. [ABSTRACT FROM AUTHOR]

Each base station in both rural and urban areas produces significant amount of carbon dioxide toxins in the surrounding environment. The volume of these toxins is proportional to the energy consumption of the base stations (BSs). In this paper, a Greedy methodology is proposed to reduce the energy consumption of the BS. The energy consumption of the BS can be achieved through the greedy algorithm in the following ways. The low traffic BS can transfer their traffic to the nearby BS, which reduces the energy consumption of the low traffic BS. The high traffic BS suffers the link failure due to its overloaded traffic in BS, which increases the energy consumption. The proposed energy efficient greedy methodology reduces the energy consumption of the BS in both criteria, by measuring traffic levels on BS and switches the modes of base stations based on their traffic levels. [ABSTRACT FROM AUTHOR]