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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.

Scientific publishing is experiencing unprecedented growth in terms of outputs across all fields. Inevitably this creates pressure throughout the system on a number of entities. One key element is represented by peer-reviewers, whose demand increases at an even higher pace than that of publications, since more than one reviewer per paper is needed and not all papers that get reviewed get published. The relatively recent Publons platform allows for unprecedented insight into the usual ‘blindness’ of the peer-review system. At a time where the world’s top peer-reviewers are announced and celebrated, we have taken a step back in order to attempt a partial mapping of their profiles to identify trends and key dimensions of this community of ‘super-reviewers’. This commentary focuses necessarily on a limited sample due to manual processing of data, which needs to be done within a single day for the type of information we seek. In investigating the numbers of performed reviews vs. academic citations, our analysis suggests that most reviews are carried out by relatively inexperienced academics. For some of these early career academics, peer-reviewing seems to be the only activity they engage with, given the high number of reviews performed (e.g., three manuscripts per day) and the lack of outputs (zero academic papers and citations in some cases). Additionally, the world’s top researchers (i.e., highly-cited researchers) are understandably busy with research activities and therefore far less active in peer-reviewing. Lastly, there seems to be an uneven distribution at a national level between scientific outputs (e.g., publications) and reviews performed. Our analysis contributes to the ongoing global discourse on the health of scientific peer-review, and it raises some important questions for further discussion.

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.

This paper builds on the findings of an earlier unpublished discussion paper titled ‘designing a model for networked carbon markets’, with its objective being to describe key elements of the mitigation value assessment process. The outcome of this paper will inform a model for the Networked Carbon Markets concept, to be prepared at a later date. The following section two acknowledges the subject matter of the NCM initiative, namely the diverse and heterogeneous trading schemes and other carbon pricing mechanisms that are being put in place by jurisdictions around the globe. As this is the subject of another World Bank paper, only a brief reference is included. Section three sets out a conceptual framework within which to consider the key elements described in the glossary of terms, addressed in section four and annexure ‘C’. This section also looks at different transaction scenarios, introducing the concepts of an international transaction unit and an index. Section five turns attention to the types of institutions that might be suitable to participate in the mitigation value assessment process, providing practical examples, and considering the types of expertise and tools those institutions might leverage. Section six considers options for regulatory supervision of the MV assessment process. Section seven looks in more detail at the relationship between mitigation value and the compliance value that might be attached to carbon assets and, in so doing, considers the role and function of the settlement platform. This section also considers the feasibility and potential benefits of an index. The concluding section eight looks at the next steps that might flow from this work.

This study analyzes how and under what conditions technical cooperation may generate larger effects on endogenous and long-term capacity development in developing countries. To this end, we use the case of national greenhouse gas (GHG) inventory in Indonesia, where the task for producing GHG inventories was first outsourced to external experts through a dedicated project, but is now managed by the Ministry of Environment and Forestry (KLHK). While investigating the long-term process through which the country developed its capacity on this issue, we evaluated how and the extent to which the five-year technical cooperation supported by Japan International Cooperation Agency contributed to this by generating catalytic effects. This paper contributes to and complements the existing literature by applying a model of strategic issue diagnosis, by which we traced the evolving issue interpretations at the ministry and their consequent actions. This study finds that the technical cooperation interacted with changes in the institutional environment, raising the issue urgency, feasibility, and interdependence as perceived at KLHK, creating momentum to change their situation, and igniting endogenous capacity development. The study highlights that, as the substantial uncertainty in their reported GHG inventories was identified through the technical cooperation, the issue came to be defined by the ministry as the problem to be solved. This paper identifies the country’s specific context as an important factor to explain a project’s catalytic effect, or the absence thereof. It emphasizes that contexts must be factored in when evaluating projects, as they are often embedded in longer timeframes and in the wider scope that goes beyond the direct beneficiaries.

From the perspective of energy consumption optimization, this paper studies the impact of green finance on carbon emissions in China. Firstly, based on the theoretical perspective, this paper explores the mechanism and path by which green finance influences carbon emissions, and analyzes the role of energy consumption in this process. Then, this paper utilizes the STIRPAT model, chain multiple mediation effect model and panel threshold model to empirically analyze the influence of green finance on carbon emissions, using provincial data from China from 2005 to 2019. The results are as follows: (1) Green finance significantly reduces carbon emissions. After accounting for potential endogeneity, this conclusion is still valid. The heterogeneity test reveals that the inhibitory effect is more remarkable in northern regions, high-carbon emission regions and energy-rich regions. (2) The results of the bootstrap test reveal that at the national level, green finance decreases carbon emissions through three paths: green technological innovation, ecological evolution of the industrial structure and green technological innovation facilitating ecological evolution of the industrial structure. Furthermore, in energy-rich regions, green finance significantly inhibits carbon emissions through all three paths, while in energy-poor regions, green finance reduces carbon emissions only through green technological innovation. (3) There is a nonlinear relationship between green finance and carbon emissions. Specifically, regardless of energy intensity or energy consumption structure, only when it is below the threshold can green finance significantly inhibit carbon emissions. Thus, realizing energy consumption optimization is an effective way to ensure the carbon emission reduction effect of green finance.

In this study, a supply chain model is proposed with preservation technology under learning fuzzy theory for deteriorating items where the demand rate depends on the selling price and also treats as a triangular fuzzy number. The deterioration rate of any item cannot be eliminated due to its natural process, but it can be controlled with the help of preservation technology. Some harmful gases are emitted during the preservation process due to deteriorating items that harm the environment. In general, it can be easily seen that most of the sellers offer a trade credit policy to their regular buyers. In this paper, the retailer’s inventory stock reduces due to demand and deterioration. It is also assumed that some units are defective due to machine defects or delivery inefficiency. The retailer accepted the policy of trade credit offered by the seller. The aim of this paper is to enhance the profit of the supply chain partners. We proposed a theorem to get the optimal values of the selling price and cycle length. The retailer’s total profit is a function of selling price and cycle length, and the retailer’s total profit is optimized with respect to selling price and cycle length under trade-credit. Numerical examples are also presented for the validation of the present study, and sensitivity analysis is also discussed to know the robustness of the supply chain model. Managerial insight and observation have been given in the sensitivity section. Limitations and future work of this paper have been presented in the conclusion section.

Since the second industrial revolution, the widespread use of various fossil fuels, such as coal, oil and natural gas, has boosted the rapid development of society and economy. However, the greenhouse gases emitted by the extensive use of energy have caused more and more serious environmental problems. In this context, optimization of the energy structure and accomplishment of the carbon-reduction targets are not just the focus and difficulty faced by China but even by the world. As a center in southwest China and a big province in energy consumption, Sichuan province’s energy consumption structure in the future will deeply influence its development under the background of accelerating industrialization and urbanization. And furthermore, the study on the relationship between energy consumption structure and carbon emissions in Sichuan province also provides a theoretical basis for the establishment of development planning in Southwest China. This paper is composed of the following parts: firstly, the characteristics of energy structure in Sichuan Province are analyzed by collecting the data of energy production and consumption over the years. Then, using the calculation method provided by IPCC, this paper calculates the carbon emissions generated by fossil energy consumption in Sichuan Province from 2005 to 2019, and analyzes its characteristics and trends. Finally, by analyzing the characteristics of the energy endowment and fossil energy consumption structure of Sichuan Province, this paper puts forward the “coal-gas transition balance theory” to discuss the impact of fossil energy consumption transition on carbon emissions, and provides guidance and ideas for the realization of emission reduction.

Poslovanje v tradicionalni obliki počasi izgublja svoj pomen, se poslavlja, zamenjali so ga novi sodobni načini, ki so znatno hitrejši, učinkovitejši, dopuščajo manj napak in omogočajo večji pregled nad delovanjem in poslovanjem podjetja. Konkurenca ima zelo velik vpliv na uspeh podjetij, ki morajo pri svojem poslovanju za obstoj uporabiti vsa možna orodja, s katerimi lahko poenostavijo in izboljšajo poslovanje ter prikažejo konkurenčne prednosti. Elektronsko poslovanje in z njim izdajanje e-računov je velik korak na tej poti k dosegu ciljev. Business in the traditional forms is slowly losing its importance and replaced by a new modern methods, which are significantly, more efficient, allowing fewer errors and allow greater oversight of the operation and performance of a company. Competition has a very larege impact on the success of the company, which must in its operations for the existence use all possible tools that can streamline operations and sho competitive advantage. Electronic commerce and its e-invoicing can be a step along the way towards the attainment of goals.

Fundamentally, newborn items that are used commercially, such as chicken, fish, and small camel, grow day by day in size and also increase their weight. The seller offers a credit policy to the buyer to increase sales for a particular growing item (fish), and in this paper, it is assumed that the buyer accepts the policy of the trade credit. In this paper, the buyer acquires the newborn items (fish) from the seller and then sells them when the newborn items have increased their size and weight. From this point of view, the present paper reveals a fuzzy-based supply chain model that includes carbon emissions and a permissible delay in payment for defective growing items (fish) under the effect of learning where the demand rate is imprecise in nature and is treated as a triangular fuzzy number. Finally, the buyer’s total profit is optimized with respect to the number of newborn items. A numerical example has been presented for the justification of the model. The findings clearly suggest that the presence of trade credit, learning, and a fuzzy environment have an affirmative effect on the ordering policy. The buyer should order more to avoid higher interest charges after the grace period, which eventually increases their profit, while at the same time, it is also beneficial for the buyer to order less to gain the benefit of the trade credit period. The fuzziness theory controls the uncertainty situation of inventory parameters with the help of a de-fuzzified method. The lower and upper deviation of demand affects the total fuzzy profit. The effect of learning gives a positive response concerning the size of the order and the buyer’s total fuzzy profit. This means that the decision-maker should be aware of the size of the newborn items, rate of learning, and trade credit period during the supply chain because these directly affect the buyer’s total fuzzy profit. The impact of the inventory parameter of this model is presented with the help of sensitivity analysis.

With the increase in carbon emissions from railway transit, green transportation has attracted worldwide attention due to its low pollution and low consumption. In order to improve the transportation efficiency of multimodal transport and reduce carbon emissions, this paper makes a systematic study on the comprehensive optimization model and method of multiple transport tasks and transport modes considering carbon emissions. Firstly, an optimization model is established with transportation distance, transportation time, and carbon emission as transportation objectives. Secondly, an improved fuzzy adaptive genetic algorithm is designed to adaptively select crossover and mutation probabilities to optimize the path and transportation mode by using population variance. Finally, an example is designed, and the method proposed in this paper is compared with the ordinary genetic algorithm and adaptive genetic algorithm, which proves the proposed model and algorithm are effective. In conclusion, it is found that the present multi-objective optimization model based on the improved genetic algorithm can adjust multimodal transport plans and reduce carbon dioxide emissions, which provides a reference basis for logistics enterprises to carry out multimodal transport.

Construction projects directly impact economies' social, economic and environmental progress. The carbon footprint of construction projects could be reduced in their various phases – conceptual, design and construction. This paper talks about ways to reduce the carbon footprint of projects during the construction phase using the case study of the development of an 11-storey high-rise building with two basements. The earth retaining and stabilizing structure (ERSS) used diaphragm walls as a retention system with a top-down construction method to facilitate basement construction. A soil improvement block, using two techniques, deep soil mixing (DSM) and Jet Grouting (JG), is constructed below the final excavation level to provide lateral support and stiffness to the retention system. The original design uses CEM I Ordinary Portland Cement (OPC) to produce cement grout in DSM and JG works. To lower the carbon footprint of the solution, the project team followed the carbon hierarchy. A low-carbon replacement for cement, namely ground granulated blast furnace slag (GGBS), is adopted as an alternative. An alternative cement CEM III/A, with 60-65% of OPC replaced with GGBS, is used while complying with the original engineering requirement - unconfined compressive strength (UCS) and stiffness modulus (E) of the DSM columns. In addition, a larger diameter DSM mixing tool is deployed, reducing the resources needed compared with the original smaller diameter. As a result, overall project carbon emissions are reduced by 7% compared to budget carbon emissions. This paper discusses the details of carbon emissions savings and operation aspects that helped achieve carbon reduction., Paper presentation

(1) Background: In light of the global economy’s digitalization and the “double carbon” target constraint, the digital economy is essential to fostering scientific and technological innovation, green growth, and lowering energy emissions. (2) Methods: This paper measures the digital economic index and carbon emission intensity and analyzes their characteristics in spatial and temporal dimensions using 282 Chinese urban panel data by improving various statistical methods of panel data, such as the entropy method, fixed effect model, multi-period DID model, moderating effect model and intermediary effect model. This paper examines the extent and mechanism of the digital economy’s impact on urban carbon emissions. (3) Results: During the sample period, the overall trend of the digital economy in China was one of constant growth, showing an unbalanced distribution pattern of “high in the eastern regions, lower in the central regions and lowest in the western regions” in the spatial dimension. Carbon emissions can be significantly decreased by the digital economy, which has a dynamic effect and an inverted U-shaped trend in its influence. The digital economy plays a significant role in reducing carbon emissions through the rational layout of industrial structures. The transmission mechanisms for the digital economy’s goal of reducing carbon emissions include environmental regulation and green technology innovation. (4) Conclusion: The research findings provide a reference for multiple decision makers to better formulate carbon emission policies and realize carbon emission decrease in the digital economy.

The present paper analyses the evolution of renewable energy generation in the European Union through a decomposition analysis based on the Index Decomposition Analysis together with the Logarithmic Mean Divisia Index within the EU27 between 2000 and 2020. A four factors decomposition approach is considered for decomposing the total RES generation and a five factors model is employed to decompose the carbon emissions. Furthermore, a focus on wind and solar photovoltaic generation is introduced through a four factors decomposition calculated in the period 2010–2020. The most influential factors affecting the RES generation trend in the period 2000–2020 are the RES share and energy efficiency wich determined an increase of 1841 TWh and a decrease of 635 TWh respectively. Variations of carbon emissions are mostly affected by the energy intensity, namely the amount of energy necessary to produce one unit of GDP, which determined a reduction of 1040 Mt. Through the decomposition analysis, the paper successfully identifies the specific drivers supporting RES development and controlling carbon emissions, thus adequate policy measures can be designed to reach planned targets.

Cette thèse, divisée en trois chapitres, contribue à la vaste et récente littérature sur l'évaluation des actifs et la finance climatique. Le premier chapitre contribue à la littérature sur la finance climatique tandis que les deux derniers contribuent à la littérature sur l'évalutaion des actifs. Le premier chapitre analyse comment les politiques environnementales visant à réduire les émissions de carbone affectent les prix des actifs et la consommation des ménages. En utilisant de nouvelles données, je propose une mesure des émissions de carbone du point de vue du consommateur et une mesure du risque de croissance de la consommation de carbone. Les mesures sont basées sur des informations sur la consommation totale et l'empreinte carbone de chaque bien et service. Pour analyser les effets des politiques environnementales, un modèle de risques de long terme est développé dans lequel la croissance de la consommation comprend deux composantes: le taux de croissance de la consommation de carbone et le taux de croissance de la part de la consommation de carbone dans la consommation totale. Ce chapitre soutient que le risque de long terme de la croissance de la consommation provient principalement de la croissance de la consommation de carbone découlant des politiques et des actions visant à réduire les émissions, telles que l'Accord de Paris et la Conférence des Nations Unies sur le changement climatique (COP26). Mon modèle aide à détecter le risque de long terme dans la consommation des politiques climatiques tout en résolvant simultanément les énigmes de la prime de risque et de la volatilité, et en expliquant la coupe transversale des actifs. La décomposition de la consommation pourrait conduire à identifier les postes de consommation les plus polluants et à construire une stratégie d'investissement minimisant ou maximisant un critère environnemental de long terme. Le deuxième chapitre (co-écrit avec René Garcia et Caio Almeida) étudie le rôle des facteurs non linéaires indépendants dans la valorisation des actifs. Alors que la majorité des facteurs d'actualisation stochastique (SDF) les plus utilisés qui expliquent la coupe transversale des rendements boursiers sont obtenus à partir des composantes principales linéaires, nous montrons dans ce deuxième chapitre que le fait de permettre la substitution de certaines composantes principales linéaires par des facteurs non linéaires indépendants améliore systématiquement la capacité des facteurs d'actualisation stochastique de valoriser la coupe transversale des actifs. Nous utilisons les 25 portefeuilles de Fama-French, cinquante portefeuilles d'anomalies et cinquante anomalies plus les termes d'interaction basés sur les caractéristiques pour tester l'efficacité des facteurs dynamiques non linéaires. Le SDF estimé à l'aide d'un mélange de facteurs non linéaires et linéaires surpasse ceux qui utilisent uniquement des facteurs linéaires ou des rendements caractéristiques bruts en termes de performance mesurée par le R-carré hors échantillon. De plus, le modèle hybride - utilisant à la fois des composantes principales non linéaires et linéaires - nécessite moins de facteurs de risque pour atteindre les performances hors échantillon les plus élevées par rapport à un modèle utilisant uniquement des facteurs linéaires. Le dernier chapitre étudie la prévisibilité du rendement des anomalies à travers les déciles à l'aide d'un ensemble de quarante-huit variables d'anomalie construites à partir des caractéristiques de titres individuels. Après avoir construit les portefeuilles déciles, cet article étudie leur prévisibilité en utilisant leurs propres informations passées et d'autres prédicteurs bien connus. Les analyses révèlent que les rendements des portefeuilles déciles sont persistants et prévisibles par le ratio de la valeur comptable sur la valeur de marché de l'entreprise, la variance des actions, le rendement des dividendes, le ratio des prix sur les dividendes, le taux de rendement à long terme, le rendement des obligations d'entreprise, le TED Spread et l'indice VIX. De plus, une stratégie consistant à prendre une position longue sur le décile avec le rendement attendu le plus élevé et à prendre une position courte sur le décile avec le rendement attendu le plus bas chaque mois donne des rendements moyens et un rendement par risque bien meilleurs que la stratégie traditionnelle fondée sur les déciles extrêmes pour quarante-cinq des quarante-huit anomalies., This thesis, divided into three chapters, contributes to the vast and recent literature on asset pricing, and climate finance. The first chapter contributes to the climate finance literature while the last two contribute to the asset pricing literature. The first chapter analyzes how environmental policies that aim to reduce carbon emissions affect asset prices and household consumption. Using novel data, I propose a measure of carbon emissions from a consumer point of view and a carbon consumption growth risk measure. The measures are based on information on aggregate consumption and the carbon footprint for each good and service. To analyze the effects of environmental policies, a long-run risks model is developed where consumption growth is decomposed into two components: the growth rate of carbon consumption and the growth rate of the share of carbon consumption out of total consumption. This paper argues that the long-run risk in consumption growth comes mainly from the carbon consumption growth arising from policies and actions to curb emissions, such as the Paris Agreement and the U.N. Climate Change Conference (COP26). My model helps to detect long-run risk in consumption from climate policies while simultaneously solving the equity premium and volatility puzzles, and explaining the cross-section of assets. The decomposition of consumption could lead to identifying the most polluting consumption items and to constructing an investment strategy that minimizes or maximizes a long-term environmental criterion. The second chapter (co-authored with René Garcia, and Caio Almeida) studies the role of truly independent nonlinear factors in asset pricing. While the most successful stochastic discount factor (SDF) models that price well the cross-section of stock returns are obtained from regularized linear principal components of characteristic-based returns we show that allowing for substitution of some linear principal components by independent nonlinear factors consistently improves the SDF's ability to price this cross-section. We use the Fama-French 25 ME/BM-sorted portfolios, fifty anomaly portfolios, and fifty anomalies plus characteristic-based interaction terms to test the effectiveness of the nonlinear dynamic factors. The SDF estimated using a mixture of nonlinear and linear factors outperforms the ones using solely linear factors or raw characteristic returns in terms of out-of-sample R-squared pricing performance. Moreover, the hybrid model --using both nonlinear and linear principal components-- requires fewer risk factors to achieve the highest out-of-sample performance compared to a model using only linear factors. The last chapter studies anomaly return predictability across deciles using a set of forty-eight anomaly variables built using individual stock characteristics. After constructing the decile portfolios, this paper studies their predictability using their own past information, and other well-known predictors. The analyses reveal that decile portfolio returns are persistent and predictable by book-to-market, stock variance, dividend yield, dividend price ratio, long-term rate of return, corporate bond return, TED Spread, and VIX index. Moreover, a strategy consisting of going long on the decile with the highest expected return and going short on the decile with the lowest expected return each month gives better mean returns and Sharpe ratios than the traditional strategy based on extreme deciles for forty-five out of forty-eight anomalies.

Human-induced carbon dioxide emissions are the major cause of climate change. Although the Philippines’ contribution to global climate change due to carbon emissions is negligible, the trend of carbon emissions from the energy sector is increasing, with emissions expected to quadruple by 2030. Thus, this paper investigates the population factors affecting per capita carbon dioxide emissions (CO2E) in the Philippines. Multiple regression analysis was performed to determine and quantify the level of effect of its population factors such as life expectancy at birth (LEB), urban population (UP), population aging (PA), and energy use per capita (EUPC) that significantly influence the CO2E. The data used for the analysis were retrieved from the World Bank for the period 1985 – 2020. Results showed that LEB, UP, PA, and EUPC are identified as significant factors affecting CO2E. Among these significant factors, EUPC has the largest effect on CO2E (F = 338.81, p = 0.000). 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Public participation is playing an increasingly important role in environmental governance. This paper uses panel data from Chinese cities to evaluate the causal relationship between public participation, regional carbon emissions, and regional carbon intensity. We obtain the following conclusions: (1) Public participation significantly reduces regional carbon emissions and regional carbon intensity, which remains robust after a series of robustness and endogeneity discussions. (2) The carbon reduction effect of public participation performs better in eastern regions, regions with higher per capita income, and regions with a concentration of tertiary industries and talents. (3) We divided the public participation into resident participation and environment non-governmental organizations (ENGOs) participation. We found an excellent interactive emission reduction effect with resident participation and a good interaction between resident participation and government and environmental organizations. (4) This paper finds that promoting regional green technology innovation is a significant mechanism for public participation in achieving carbon emission reduction. (5) Finally, this paper found an “inverted U-shaped” non-linear relationship between public participation and regional carbon emissions. The results reveal the importance of public participation in regional carbon emissions and provide an empirical basis for promoting informal environmental regulation.

Robot usage has become an intrinsic requirement to drive the intelligence transformation of the manufacturing industry, providing a key driver for carbon emission reduction in China’s manufacturing industry. This paper examines the carbon emission reduction effect of robot usage at three levels: theoretical, empirical, and decomposition. On the basis of the pollution emission data of segments of China’s manufacturing industry from 2006 to 2020, the paper takes matching robot stock and incremental data from the International Federation of Robotics statistics, and applies the instrumental variable method to identify and estimate the role played by industry robot stock and incremental improvement in reducing pollution emissions. The empirical results show that the usage of robots does significantly reduce the carbon emissions of the manufacturing industry in China. Further, mechanism testing revealed that robot use reduces corporate pollution emissions mainly through channels such as R&D investment and manual substitution. The research provides microscopic evidence for objectively assessing the impact of robot use on environmental pollution emissions from the perspective of promoting robot applications, and suggests policy recommendations for reducing pollution emissions from China’s manufacturing sector, which can help achieve harmonious economic and environmental development.

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.

Cultivated land is a vital strategic resource to ensure human survival and development. It is pertinent to introduce the environmental constraint index into the study of cultivated land use efficiency and promote the coordinated development of environmental and economic benefits. Based on the construction of the evaluation index system of cultivated land use efficiency, including carbon emission as the undesired output, this paper used the super efficiency MinDS model to measure the cultivated land use efficiency of China by using the data from 2009 to 2019. The results show the following. (1) During 2009–2019, the overall use efficiency of cultivated land in China showed a tendency to fluctuate and increase, ranging from 0.871 to 0.948, indicating high use efficiency. Eleven provinces had an average land use efficiency of more than 1. (2) Among the input–output indicators, the carbon emission indicator showed the largest average percentage of improvement at 15.21%, followed by the pesticide index and agricultural machinery index, and the smallest average improvement was the irrigation indicator at 3.55%. (3) There were apparent differences in the average relaxation and improvement proportion of input–output indicators of cultivated land use in the provinces. (4) China’s cultivated land use efficiency has absolute σ-convergence, absolute β-convergence and conditional β-convergence, which means that the difference in cultivated land use efficiency between provinces has a tendency to decrease, and that China’s cultivated land use efficiency will stabilize. This paper provides a clear direction for the promotion and improvement of cultivated land use efficiency in China.

As the world’s largest carbon emitter, China is facing great pressure to reduce emissions. With the country’s proposed timeline for carbon peaking and carbon neutralization, a new goal has been established for China’s low-carbon development. Based on the improved equal proportion allocation method, this paper allocates the overall carbon emission control goal for 2025 among 30 provinces and cities, based on 2015 figures, and measures and studies the country’s carbon emission allocation efficiency on this basis. The results show that Beijing, Tianjin, Hebei, Shandong, Zhejiang, Shanghai, Jiangsu, Guangdong and Inner Mongolia need to increase their emission reduction capacity, while Jiangxi, Guizhou, Gansu, Qinghai, Hainan and Guangxi have relatively low emission reduction targets. Based on this allocation scheme, more provinces can reduce carbon emissions by increasing their efficiency with up-to-date technology, and a new vision for national allocation that is more easily accepted by all provinces and regions can be developed. Based on the research results of this paper, each province and region can choose its own low-carbon economic development path within the constraints of China’s carbon intensity emission reduction targets, without compromising its own economic development characteristics.

Based on panel data from 2000 to 2017 in 29 Chinese provinces, this paper analyzes the impact of industrial structure upgrading on carbon emissions by constructing a spatial panel model and a panel threshold model. The results show that (1) there is a significant spatial correlation between carbon emissions in Chinese provinces, and the carbon emissions of a province are affected by the carbon emissions of surrounding provinces; (2) in China, carbon emissions have a significant time lag feature, and current carbon emissions are largely affected by previous carbon emissions; (3) industrial structure upgrading can effectively promote carbon emission reductions in local areas, and the impact of industrial structure upgrading on carbon emissions has a significant threshold effect. With continued economic development, the promotion effect of industrial structure upgrading on carbon emission reductions will decrease slightly, but this carbon emission reduction effect is still significant. (4) In addition, there is a clear difference between the impact of energy consumption intensity and population size on carbon emissions in short and long terms. In the short term, the increase in energy consumption intensity and the expansion of population size not only increase the carbon emissions of a local area but also increase the carbon emissions of neighboring areas. In the long term, the impact of energy consumption intensity and population size on carbon emissions of neighboring areas will be weakened, but the promotion impact on carbon emissions in local areas will be strengthened. HIGHLIGHTS This paper studies the impact of industrial structure upgrading on carbon emissions in detail.; This paper uses a static spatial panel model and a dynamic spatial panel model for analysis.; This paper uses economic development as a threshold variable to determine whether the impact direction and degree of industrial structure upgrading on carbon emissions will change with economic development.

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.

China’s power industry is a major energy consumer, with the carbon dioxide (CO2) generated by coal consumption making the power industry one of the key emission sectors. Therefore, it is crucial to explore energy conservation and emissions reduction strategies suitable for China’s current situation. Taking a typical cogeneration enterprise in North China as an example, this paper aims to establish a generalized regression prediction model for carbon emissions of coal-fired power plants, which will provide a reference for China to seek strategies for carbon peaking and carbon neutralization in the future. Firstly, in terms of the selection of influencing factors, this paper uses objective index screening methods, simulation means, and the eXtreme Gradient Boosting algorithm (XG-Boost) to analyze the feature importance of various influencing factors. It is concluded that the relevant influencing factors of the boiler feed water system have a strong correlation and characteristic importance with the carbon emissions results of coal-fired power plants. Therefore, this paper proposes to introduce these factors into the regression prediction model as auxiliary variables to more scientifically reflect the carbon emissions results of coal-fired power plants. Secondly, in the aspect of regression prediction model establishment, inspired by the sparrow’s foraging behavior and anti-predation behavior, this paper selects the sparrow search algorithm (SSA) with strong optimization ability and fast convergence speed to optimize the super parameters of the long short-term memory network algorithm (LSTM). It is proposed to use the SSA-LSTM algorithm to establish the carbon emissions regression prediction model of coal-fired power plants. The advantage of the SSA-LSTM algorithm is that it can effectively simplify the super parameter selection process of the LSTM algorithm, effectively solve the global optimization problem, prevent the model from falling into overfitting and local optimization, and make the carbon emissions regression prediction model of coal-fired power plants achieve a better fitting effect. By comparing the performance indicators of the model before and after the improvement, it is found that the regression prediction effect of the SSA-LSTM coal-fired power plant carbon emissions regression prediction model, which introduces boiler feed water influencing factors, has been effectively improved. Therefore, the model proposed in this paper can be used to conduct a comprehensive impact factor analysis and regression prediction analysis on the carbon emissions intensity of China’s coal-fired power plants, formulate targeted carbon emissions reduction countermeasures, and provide a theoretical basis for energy conservation and emissions reduction of China’s coal-fired power plants.

Governments across the world are taking actions to address the high carbon emissions associated with the construction industry, and to achieve the long-term goals of the Paris Agreement towards carbon neutrality. Although the ideal of the carbon-emission reduction in building projects is well acknowledged and generally accepted, it is proving more difficult to implement. The application of building information modeling (BIM) brings about new possibilities for reductions in carbon emissions within the context of sustainable buildings. At present, the studies on BIM associated with carbon emissions have concentrated on the design stage, with the topics focusing on resource efficiency (namely, building energy and carbon-emission calculators). However, the effect of BIM in reducing carbon emissions across the lifecycle phases of buildings is not well researched. Therefore, this paper aims to examine the relationship between BIM, carbon emissions, and sustainable buildings by reviewing and assessing the current state of the research hotspots, trends, and gaps in the field of BIM and carbon emissions, providing a reference for understanding the current body of knowledge, and helping to stimulate future research. This paper adopts the macroquantitative and microqualitative research methods of bibliometric analysis. The results show that, in green-building construction, building lifecycle assessments, sustainable materials, the building energy efficiency and design, and environmental-protection strategies are the five most popular research directions of BIM in the field of carbon emissions in sustainable buildings. Interestingly, China has shown a good practice of using BIM for carbon-emission reduction. Furthermore, the findings suggest that the current research in the field is focused on the design and construction stages, which indicates that the operational and demolition stages have greater potential for future research. The results also indicate the need for policy and technological drivers for the rapid development of BIM-driven carbon-emission reduction.

In recent years, the issue of regional economics and carbon emissions has become a research hotspot in the cross field of economy, environment and ecology. This paper selects the regional economics and carbon emissions related literature collected in the Web of Science (WOS) database as the basis, and uses the bibliometric software Citespace and VOSviewer to visually analyze the time distribution, organization, author and keywords in this research field. This paper provides a more systematic analysis of how different regions of China could achieve carbon emission objectives, from the aspects of regional industrial transformation, energy consumption structure, policy implementation and regional coordinated development. The keywords with high frequency are carbon emissions, economic growth and energy consumption, etc. The research hotspots can be divided into structural decomposition analysis, low-carbon industry transformation path, policy framework and energy efficiency, etc. The results show that future research should strengthen multidisciplinary cross-integration in different universities and institutions. However, based on in-depth analysis, the key factors which affect regional carbon emissions are regional policy implementation, changes in industrial structures, optimization of energy consumption structure and carbon trade market mechanism. Finally, we suggest that institutions and scholars should conduct adequate interdisciplinary and cross-industry cooperation; industrial sector development should consider local endowment; there should greater use of clean energy to optimize the energy consumption structure; and an increase in R&D carbon capture and sequestration technology.

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.

Voluntary corporate environmental disclosure has increased significantly in the last decade. However, the increase in environmental disclosure has also been accompanied by the social questioning of its veracity. Previous studies have mainly focused on the determinant factors behind corporate decisions to disclose environmental data, with only limited consideration of both carbon performance and the veracity of the information disclosed. Based on an international sample of firms from 12 countries, this paper analyzes the impact of regulative pressures related to climate change on the likelihood of companies engaging in greenwashing. The results show that the number of regulations related to climate change negatively influences the propensity of firms to engage in greenwashing. Furthermore, firms in countries with stringent climate-related regulations are less likely to participate in greenwashing practices. This paper adds to the existing literature concerning greenwashing by demonstrating that institutional theory can deliver further insights into the explanation of corporate greenwashing behavior. This is the first study to incorporate international climate-related regulations into the analysis of corporate greenwashing. It also provides a new method for identifying greenwashing firms, based on their carbon performance and disclosure., This study has been supported by the Universidad de Sevilla VI PPIT [IV.3, 2018].

This paper uses the entropy method to estimate China’s green financial development from four aspects, namely, green credit, green securities, green insurance, and green investment, based on the provincial-level panel data from 2008 to 2019. The spatial Durbin model (SDM) is adopted to estimate the spatial effect of green finance on carbon emissions. We then compare the heterogeneous effect in the South and North of China. The results show that China’s green financial development can significantly reduce carbon emissions, and regional heterogeneities are obvious. In the South of China, this effect from local and adjacent regions is not significant, while on the whole, green finance can significantly reduce carbon emissions; but for Northern China, this effect is not significant; nationally, the development of green finance and carbon emissions in adjacent areas showed an inverted U-shaped relationship. China’s green financial development and carbon emissions also showed an inverted U-shaped relationship. These results suggest that the effect of green finance development on carbon emissions exhibits substantial regional heterogeneity in China. Our paper provides some concrete empirical evidence for policymakers to formulate green financial policies to achieve the double carbon goal in China.

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies.

Carbon emissions have become a new threat to sustainable development in China, and local government actions can play an important role in energy conservation and emission reduction. This paper explores the theoretical mechanisms and transmission paths of economic growth targets affecting carbon emissions from the perspective of economic growth targets and conducts an empirical analysis based on 30 provincial panel data in China from 2003 to 2019. The results show that: economic growth targets are positively correlated with carbon emissions under a series of endogeneity and robustness; there are regional heterogeneity, target heterogeneity and structural heterogeneity in the impact of economic growth targets on carbon emissions; after economic growth targets are set, government actions can influence carbon emissions by affecting resource mismatch and industrial restructuring; It is further found that there is a “U” shaped relationship between economic pressure and carbon emissions. Based on the above findings, this paper further proposes that a high-quality performance assessment mechanism should be developed to bring into play the active role of local governments in achieving carbon reduction goals, and thus contribute to high-quality economic development.

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.

The income gap and global warming have always been topics of common concern to scholars worldwide. Internationally, there is no consensus yet about the impact of the income gap on carbon emissions, and there are few studies about that in China. To explore the effect of the income gap on carbon emissions at the provincial level in China, this paper first theoretically and qualitatively analyzes the non-linear impact of the income gap on carbon emissions. Then, the Gini coefficient of the resident income of different regions in China from 2010 to 2019 is calculated. Finally, a threshold regression model is used to quantitatively test the existence of a threshold effect between the income gap and carbon emission intensity in China. The threshold value is the per capita disposable income of residents. The results show that the income gap is positively related to carbon emission intensity in poor regions. In high-income areas, the widening income gap inhibits the increase in carbon emission intensity. Based on this, this paper proposes policy recommendations to narrow the income gap and reduce the intensity of carbon emissions.

Ekonomikos globalizacijos kontekste susijungimų ir įsigijimų sandoriai tapo dažna verslo plėtros priemone. Kasmet didėja įmonių susijungimų ir įsigijimų skaičius bei yra pastebima jų vertės augimo tendencija. Daug tokių sandorių įvyksta elektros energetikos sektoriuje, kuris, be to, į aplinką išskiria daugiausiai anglies dioksido. Tai lemia temos aktualumą bei poreikį tirti, kokį poveikį susijungimo ir įsigijimo sandoriai turi elektros energetikos sektoriaus įmonių finansiniams rezultatams bei anglies dioksido emisijai. Šio baigiamojo projekto tyrimo objektas – įmonių susijungimų ir įsigijimų sandoriai. Darbo tikslas yra parengti vertinimo modelį ir įvertinti susijungimų ir įsigijimų sandorių poveikį įmonių finansiniams rezultatams ir CO2 emisijos kiekiams. Pirmoje projekto dalyje buvo atlikta įmonių susijungimų ir įsigijimų ir CO2 emisijos vertinimo problematika. Antroje dalyje buvo išanalizuota mokslinė literatūra, išskirti teoriniai sprendimai, naudojami finansiniam efektyvumui ir CO2 emisijai įmonių susijungimų ir įsigijimų sandorių kontekste įvertinti. Remiantis mokslinės literatūros analize, trečioje dalyje suformuluota tyrimo metodologija, apimanti tris tyrimo etapus: apskaitos metodo taikymą, įvykių tyrimo metodo taikymą bei DEA ir regresijos modelių sudarymą. Ketvirtoje projekto dalyje pristatyti tyrimo rezultatai. Jie parodė, kad įmonių susijungimų ir įsigijimų sandoriai neigiamai veikia įsigyjančių įmonių likvidumo rodiklius, tačiau teigiamai paveikia pelningumo, įmonių vertės ir finansinės rizikos rodiklius. Šis poveikis statistiškai reikšmingai pasireiškė tik ketvirtadalyje imties. Taip pat buvo išsiaiškinta, kad įsigyjančių įmonių perteklinę akcijų grąžą po paskelbimo apie susijungimo ar įsigijimo sandorį, išsivysčiusiose ir besivystančiose rinkose įvykis paveikia skirtingai. Taip besivystančiose rinkose jų pokyčiai yra labiau chaotiški, negu išsivysčiusiose rinkose, tuomet kai pastarosiose paskelbus apie sandorį, įmonės pradėjo tolygiai kaupti perteklinę grąžą. Be to, tyrimas parodė sąsają tarp susijungimo ar įsigijimo sandorių ir įsigyjančių įmonių anglies dioksido emisijos. Buvo išsiaiškinta, kad po sandorių ilgalaikėje (3 metų) perspektyvoje atsirado daugiau įmonių, kurios į aplinką išskirdamos mažiau anglies dioksido, uždirba daugiau pelno. Tyrime sudaryta regresijos lygtis parodė, jog CO2 emisija, pagrindinės veiklos sąnaudos ir turtas turi statistiškai reikšmingą ryšį su įsigyjančios įmonės pajamomis., In the context of economic globalization, mergers and acquisitions have become a common tool for business development. The number of mergers and acquisitions is growing every year and there is a growing tendency in their value. The majority of these transactions take place in the electricity sector, which also emits the most carbon dioxide. This determines a need to examine the impact of mergers and acquisitions on the financial performance and CO2 emissions of companies in the electricity sector. The object of a current research is mergers and acquisitions. The aim of the paper is to develop a valuation model and to evaluate the impact of mergers and acquisitions on corporate financial performance and CO2 emissions. The problematics of evaluating mergers and acquisitions and CO2 emissions has been addressed in the first part of the project. Scientific literature analysis, discernment of theoretical solutions which are used to evaluate corporate financial performance and CO2 emissions in the context of mergers and acquisitions has been performed in the second part of this paper. Based on the analysis of financial literature, the third part consists of formulation of the research methodology covering three stages of this project: application of accounting research method, event study method, DEA and regression analysis. Results and discussion are presented in the fourth part of the project. The results of the study showed that mergers and acquisitions have a negative impact on the liquidity ratios of the acquiring companies, but have a positive impact on profitability, corporate value and financial risk ratios. This effect was statistically significant in a quarter of the sample. It has also been found that the abnormal return of acquiring companies after the announcement of a merger or an acquisition is affected differently in developed and emerging markets. In emerging markets such changes were more chaotic than in developed markets. Also, they changed the trend often, when in the developed markets companies began to accumulate positive abnormal returns gradually after the announcements. In addition, the study showed a relation between mergers and acquisitions and carbon emissions of the acquiring companies. It was found that after completing a deal in the long run (3 years) more eco-efficient companies, which earned more profit while emitting less carbon dioxide, appeared. The regression analysis showed that CO2 emissions, operating expenses and assets have a statistically significant relationship with the acquirer's revenue.

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.

There is a global call for a paradigm shift in the construction industry towards carbon neutrality, but a scant effort has been madein practice, especially concerning circularity. This paper helps bridge the gap by introducing a parametric approach to optimize sustainable construction design. The methodology was tested on a newly constructed office building, inspired by circularity principles, in Westerlo, Belgium. The methodology consists of parametric construction-typological analysis, automated through One Click LCA software (Life Cycle Assessment) and Microsoft Excel with 21 alternate designs and 630 iterations. The parametric variations involved three key performance indicators: construction system, materials’ environmental impact, and materials; reuse of content. The environmental effects of both construction systems (i.e., structural system, foundation type, materials, and envelope details) and reused building materials content (i.e.,) were evaluated by the parametric analysis for four construction systems scenarios. Environmental impact analysis for timber, steel, concrete, and hybrid construction systems was conducted, following ISO 14040 and CEN/TC 350 standards. The focus of the whole life cycle assessment was mainly on carbon neutrality. Results indicate that using local biosourced materials, including timber, can remarkably reduce buildings’ environmental impact. The sensitivity analysis results provide hard evidence that the construction material’s weight, materials reuse potential, and construction dismantling ability are the most influential factors in carbon-neutral buildings. This paper should improve professionals’ understanding of the impact of different structural systems choices and inform building designers about the circularity potential, and carbon footprint of construction technologies.

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.

Promoting carbon emissions of the transportation industry to reach the peak as soon as possible is an effective way to control carbon emissions in China. Combined with the logarithmic mean Divisia index (LMDI) model and the Tapio decoupling model, this paper tries to predict and analyze the time, path, and quality of carbon peaking in the transportation industry in China and its eastern, central, and western regions. The research shows the following: 1) Under the “benchmark scenario,” the transportation industry in China and its three regions will not achieve the international commitment of achieving peak carbon emissions by 2030. 2) Under the “radical scenario,” the peak time of China’s transportation industry can be reached in 2027, which is 5 years earlier, 10% peak value lower than that in the “benchmark scenario.” In that scenario, in transportation industry, the eastern region will reach the carbon peak in 2025 and the central and western regions will both reach the carbon peak in 2028. By then, the peak quality of carbon emissions will be better than that under the “benchmark scenario” in transportation industry. This paper can provide theoretical support for promoting carbon emission reductions and carbon peaking in China’s transportation industry.