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As the tragedy in Ukraine deepens, it's clear that the world should end its dependency on Russian oil. It will be ironic, however, if nations disentangling themselves from this compromised energy source instead turn to another energy source with destructive impacts: harvesting and burning forest wood for fuel, which increases carbon emissions compared to fossil fuels, and degrades forests. [ABSTRACT FROM AUTHOR]

Climate change and the resulting higher frequency of extreme weather events have a direct negative impact on mental health. Natural disasters are particularly associated with an increase in the prevalence of depression, anxiety and posttraumatic stress disorder. Indirect consequences of climate change, such as food shortages, economic crises, violent conflicts and forced migration, additionally represent severe psychological risk and stress factors. Climate anxiety and solastalgia, the distress induced by environmental change, are new psychological syndromes in the face of the existential threat posed by the climate crisis. Accordingly, a sustainable psychiatry must prepare for increasing and changing demands. The principles of psychiatric treatment need to focus more on prevention to reduce the overall burden on the healthcare system. Waste of resources and CO2 emissions in psychiatric treatment processes as well as infrastructure must be perceived and prevented. Psychiatric education, training and continuing education concepts should be expanded to include the topic of climate change in order to comprehensively inform and sensitize professionals, those affected and the public and to encourage climate-friendly and health-promoting behavior. More in-depth research is needed on the impact of climate change on mental health. The DGPPN becomes a sponsor and aims for climate neutrality by 2030 by committing to climate-friendly and energy-saving measures in the area of finance, in relation to the DGPPN congress as well as the DGPPN office. [ABSTRACT FROM AUTHOR]

Abstract Although paper production demonstrates high energy and water sensitivity in their life cycles, only a few systematic analyses have focused on these issues. Therefore, an energy and carbon coupled water footprint analysis of five types of Kraft wood pulp-based paper (i.e., specialty-, art-, household-, light coated-, and tissue paper) is conducted in this study to help improve the environmental performance of the paper industry. All investigations are conducted with an impact-oriented approach based on ISO standards. Results show that elemental-chlorine-free bleaching is more environmentally friendly than chlorination-alkaline extraction-hypochlorite bleaching, specifically for global warming, aquatic eutrophication, and human health (i.e., carcinogens and non-carcinogens). Gray water footprint along whole life cycles of each product is approximately twice of blue water footprint. Direct processes contribute approximately 50% to water scarcity, while their impact on aquatic eutrophication ranges from 0.002% to 50.72%. For other midpoints, indirect processes dominate the influences. Meanwhile, COD, BOD 5 , CO 2 , TP, Cr (Ⅵ), Ti, Hg, and As are key contributors. Finally, reusing sludge by direct burning in the recovery furnace, reclaiming organic compounds in black liquor before alkali recycling, and integrating black liquor gasification technology are expected to provide substantial environmental benefits. Amelioration of wastewater treatment, optimization of the national energy structure, and improvement of the efficiency of chemicals and freshwater are recommended. Highlights • Energy and carbon coupled water footprint analysis of Kraft pulp paper is applied. • The environmental benefit of black liquor recovery is dominated by reused alkali. • Direct processes contribute a lot to water scarcity and aquatic eutrophication. • COD, BOD, TP, and heavy metals are key substances. • Optimizing waste reuse and availability efficiency of chemicals is recommended. [ABSTRACT FROM AUTHOR]

With the continuous development of the global economy, carbon dioxide and other greenhouse gas emissions are persistently increasing, making global warming an indisputable fact. As a high-energy consuming industry, the building industry has gradually emerged as the primary source of greenhouse gas emissions during urban expansion. Consequently, countries are exploring sustainable development pathways for low-carbon buildings to minimize the detrimental impact caused by the construction industry. This paper summarizes the current status of low-carbon building development and, through literature analysis concerning carbon standard systems and carbon emission accountings, discusses the challenges and possible improvements for the future. Establishing a quantitative evaluation tool for carbon emissions and elucidating accounting methods in the construction field is fundamental and a prerequisite for comprehensively studying low-carbon buildings throughout their life cycle. The challenges of low-carbon building development are as follows: (1) lack of a set of carbon emission measurement standards that can be commonly used internationally, (2) lack of a deep and systematic study of the theory of carbon emission accountings and (3) difficulty in recognizing carbon emission boundaries and related data for existing carbon emission accounting methods. This paper provides a comprehensive analysis of the current progress in low-carbon building development, along with an examination and optimization of the application of carbon emission accounting methodologies within construction to address the challenges. [ABSTRACT FROM AUTHOR]

As the world embarks on a global mission to tackle climate change, reducing carbon represents a key challenge given the escalating global warming. The U.K. is among many other nations that are determined to decarbonise all sectors and strive to achieve a net zero carbon target by 2050. While much attention has been paid to improving performance and reducing carbon emissions in electrical machines, the current research landscape focuses mainly on the thermal and electromagnetic facets. Surprisingly, carbon emissions from the production stage, especially those related to raw material consumption, remain a largely unexplored area. This paper wishes to shed light on a neglected dimension by providing a comprehensive review of carbon emissions in the manufacture of electrical machines, thus contributing significantly to the wider discourse on carbon emission reduction by comparing the carbon emission values associated with various materials commonly used for the main components of these machines. A further case study is included to assess and explore the impact of material alterations on a synchronous machine, from a carbon emission perspective. A reliable material guide will provide engineers at the design stage with the critical insight needed to make informed material selection decisions, highlighting the critical role of carbon emission values beyond conventional thermal and electromagnetic considerations, achieving sustainable and environmentally conscious electrical machine design. [ABSTRACT FROM AUTHOR]

The serious consequences of climate warming have increasingly become a globe agenda in recent decades. China has been actively participating in various initiatives to address global climate change and has made commitments to reduce carbon emissions. Although the logistics industry is regarded as the "new driving force of national economic development", its carbon intensity is relatively high. Therefore, whether the logistics industry can develop in a green and low-carbon way is very crucial. This paper takes the green logistics efficiency of China's provincial logistics industry as the research object. The Super-SBM model is used to measure the China's green logistics efficiency, then the general dynamic characteristics is depicted by kernel density analysis. With the GML (Global Malmquist-Luenberger) index model, the reasons for the changes in green logistics efficiency are explained. Finally, Moran's I index is used to analyze the spatial correlation of green logistics efficiency in each province. The results show that the green logistics efficiency in China is at a low level, but with an upward trend. China's green logistics efficiency has a significant positive spatial correlation, showing a zonal pattern of high in the east and low in the west, and a polarization phenomenon. In addition, the bottleneck of the overall development of green logistics efficiency in China depends on the level of green technology. Furthermore, the results also imply that green technology advancement is an inherent key factor for green logistics efficiency to achieve growth. [ABSTRACT FROM AUTHOR]

Since the last century many of the governmental and nongovernmental organizations have recognized the need of reducing the greenhouse gas (GHG) emissions and were taking measures to mitigate the effects on the global warming. The manufacturing industry leaders have started to act on GHG reduction and set up specific targets and mitigation measures to contribute to limiting the global warming as much as possible. Identifying the corporate carbon footprint by emission scope classified in: Scope 1, 2 and Scope 3 is very challenging and really demanding process but at the same time is the key step to reduce the carbon emissions. The Greenhouse gas protocol (GHG protocol) is a global standard framework for emission management in operations and supply chain. The purpose of this paper is to identify and calculate the carbon footprint in a production facility according the GHG protocol. The paper covers direct emissions generated by sources owned or controlled by the facility, and indirect emissions generated by purchased electricity monitored during a period of one year. The data are analyzed and after mitigation measures to reduce carbon footprint are proposed. The results present that emissions generated by purchased electricity have the largest contribution to the overall company direct and indirect emissions. Based on the results the recommendations for improvement are focused on reducing electricity consumption and use of renewable energy sources. This approach and the proposed actions can be implemented in manufacturing facilities operating in various types of industries. [ABSTRACT FROM AUTHOR]

Background: Carbon footprint is a widely used tool to measure the impact of human activities on global warming. The lockdowns caused by the COVID-19 pandemic have significantly changed energy consumption forms and decreased CO2 emissions worldwide. This research is a trial to elaborate the effect of COVID-19 pandemic on the carbon footprint of the High Institute of Public Health (HIPH). Objective(s): The present study aimed at measuring the amount of water, electricity, fuel, and paper consumption by HIPH before and during the emergence of COVID-19 and assessing the carbon footprint of the HIPH population inside the building through the same period. Methods: A cross-sectional survey was done using a pre-designed questionnaire targeting 10 % of the HIPH population before and during the pandemic. Bills of water, electricity, paper, and fuel consumption were used to calculate the carbon footprint for one year before and one year during COVID-19. Results: Online responses increased during COVID-19 emergence (69.2% during COVID-19 pandemic versus 44.1% before COVID-19 pandemic). Females were more than 2/3 of the respondents (70.6%). There was a significant difference in traveling outside Egypt before and during COVID-19 (X2=12.794, p-value=0.002). A significant reduction in the average time spent in front of the computer at HIPH was found during the emergence of COVID-19 as most of the work became from home (X2= 18.443, p-value= 0.001). Significant reduction in the consumption of hot drinks, cold drinks, bottled water and food inside the HIPH was noticed (X2=50.219, p-value<0.0001; X2=12.030, p-value=0.017; X2=15.945, p-value=0.004; X2=72.929, p-value<0.0001 respectively). The carbon footprint of HIPH in the period from July 2018 to June 2019 was 79.43MT of CO2e. In the period from July 2020 to June 2021, it was 59.85MT of CO2e with a 25% reduction in the emission. Conclusion: The carbon footprint of HIPH was reduced during the lockdown period compared to that before the epidemic. So, efforts should be gathered to hasten the reduction of carbon footprint through encouraging online teaching and changing lifestyle. [ABSTRACT FROM AUTHOR]

As global warming intensifies, reducing carbon emissions has become a global common mission. Tourism transportation is one of the important sources of carbon emissions, and reducing its carbon emissions is a key part of achieving China's carbon reduction goals. Based on the panel data of various provinces and cities in North China from 2000 to 2022, this paper calculates the carbon emissions of tourism transportation by using the carbon emission coefficients of different transportation modes in different segments. Moreover, the temporal and spatial evolution of the tourism economy is systematically analyzed. The Tapio decoupling model and LMDI addition decomposition model are used to analyze the relationship between carbon emissions and tourism economic growth and the effects of 11 influencing factors on carbon emissions. The results show that: (1) The carbon emission of tourism transportation in North China has experienced four stages: a steady growth period, a transitional adaptation period, a stable equilibrium period, and a drastic decline period. The overall carbon emission level of tourism transportation is as follows: Hebei Province > Shanxi Province > Inner Mongolia Autonomous Region > Beijing City > Tianjin City. (2) The decoupling coefficient between tourism traffic carbon emissions and economic development fluctuates but mainly shows a weak decoupling state. (3) In terms of influencing factors, passenger size and passenger density have the greatest impact on the carbon emissions of tourism transportation. [ABSTRACT FROM AUTHOR]

With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact. Optuna–LightGBM–XGBoost, a novel power and carbon emission relationship model that aims to improve the efficiency of carbon emission monitoring and estimation for power generation companies, is proposed in this paper. Deeply exploring the intrinsic link between power production data and carbon emissions, this model paves a new path for "measuring carbon through electricity", in contrast to the emission factor method commonly used in China. Unit data from power generation companies are processed into structured tabular data, and a parallel processing framework is constructed with LightGBM and XGBoost, and optimized with the Optuna algorithm. The multilayer perceptron (MLP) is used to fuse features to enhance prediction accuracy by capturing characters that the individual models cannot detect. Simulation results show that Optuna–LightGBM–XGBoost can achieve better performance compared to existing methods. The mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) of the model are 0.652, 0.939, 0.136, and 0.994, respectively. This not only helps governments and enterprises to develop more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for achieving global carbon neutrality goals. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy, China has become the world's largest carbon emitter. China not only has an obvious growth rate of industrial carbon emissions but also the intensity of agricultural carbon emissions is hovering at a high level. The development of China's agricultural economy has largely come at the expense of high emissions. Currently, under the background of global warming and difficulty in controlling greenhouse gas emissions, the development of low-carbon agriculture is an important way to realize the harmonious development of the ecological environment and economic growth and to promote the sustainable development of agriculture. The agricultural production efficiency is the main factor affecting the intensity of agricultural carbon emissions. Based on provincial panel data of China from 2010 to 2019, this paper establishes an indicator system and uses the super-efficiency SBM model to measure agricultural production efficiency. The regional agricultural carbon emissions were estimated using carbon-emission-related agricultural production activities. In order to study the nonlinear relationship between agricultural production efficiency and agricultural carbon emission intensity in the narrow sense, this paper uses a threshold regression model with agricultural carbon emissions as the threshold variable. Based on the analysis of China's agricultural production efficiency and agricultural carbon emissions from 2010 to 2019, an empirical test is conducted through a threshold regression model. The results show an "inverted U-shaped" relationship between agricultural production efficiency and agricultural carbon emission intensity. In areas with high agricultural production efficiency, the improvement of production efficiency can suppress the intensity of agricultural carbon emissions; in areas with low agricultural production efficiency, the improvement of production efficiency increases the intensity of agricultural carbon emissions. Finally, based on the research conclusions, this paper provides feasible suggestions and countermeasures for China's agricultural carbon emission reduction and improvement of agricultural production efficiency. [ABSTRACT FROM AUTHOR]

As many business activities—especially those associated with the energy-intensive industries—continue to be major sources of greenhouse gas emissions, and hence significantly contributing to global warming, there is a perceived need to identify ways to make business activities eventually carbon neutral. This paper explores the implications of a changing climate for the global tourism business and its intertwining global aviation industry that operates in a self-regulatory environment. Adopting a bibliometric analysis of the literature in the domain of global tourism and climate change (772 articles), the paper reveals the underlying sustainability issues that entail unsustainable energy consumption. The aviation industry as a significant source of carbon emission within the sector is then examined by analyzing the top 20 largest commercial airlines in the world with respect to its ongoing mitigating measures in meeting the Paris Agreement targets. While self-regulatory initiatives are taken to adopt Sustainable Aviation Fuels (SAF) as alternative fuel production and consumption for drastically reducing carbon emission, voluntary alignment and commitment to long-term targets remain inconsistent. A concerted strategic approach to building up complementary sustainable infrastructures among the global network of airports based in various international tourist destination cities to enable a measurable reduction in carbon emission is necessary to achieve a transformational adaptation of a business sector that is of essence to the recovery of the global economy while attempting to tackle climate change in a post-COVID-19 era. [ABSTRACT FROM AUTHOR]

Global warming has endangered the natural ecosystem's balance, as well as human existence and development, and it is mostly caused by carbon dioxide. Identifying carbon emission characteristics and predicting carbon emission reasonably is helpful to provide indication for the effective design of emission reduction path. The most literature use a single prediction model; this paper predicts carbon emission using a number of strategies based on previous research. Considering the prediction accuracy, advantages, and disadvantages of each method, a new method combining autoregressive integrated moving average (ARIMA) model and NAR neural network (NAR-NN) is proposed; in addition, this paper attempts to explain the carbon emission characteristics and emission reduction paths of each region from the new perspective of clustering. First, the results show that China's carbon emission features can be divided into four categories: low-carbon demonstrative type, low-carbon potential type, high-carbon developed type, and high-carbon traditional type. Moreover, low-carbon demonstrative type includes merely Beijing and Shanghai, low-carbon potential type is distributed in the southeast coastal areas of China, the high-carbon developed type is mainly distributed in Northeast China, and the western region basically belongs to high-carbon traditional type. Second, ARIMA model and NAR-NN are the two best methods in terms of prediction effect, and the combined model has better prediction effect than the single model. Third, carbon emissions in most regions of China will increase in the next few years; the time of carbon peak in the east is earlier than that in the west regions of China. Beijing will probably be the first region in China to complete the carbon peak. Besides, there is a certain correlation between the carbon peak time and the type of carbon emission in each region. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

It is essential to predict carbon prices precisely in order to reduce CO2 emissions and mitigate global warming. As a solution to the limitations of a single machine learning model that has insufficient forecasting capability in the carbon price prediction problem, a carbon price prediction model (GWO–XGBOOST–CEEMDAN) based on the combination of grey wolf optimizer (GWO), extreme gradient boosting (XGBOOST), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is put forward in this paper. First, a random forest (RF) method is employed to screen the primary carbon price indicators and determine the main influencing factors. Second, the GWO–XGBOOST model is established, and the GWO algorithm is utilized to optimize the XGBOOST model parameters. Finally, the residual series of the GWO–XGBOOST model are decomposed and corrected using the CEEMDAN method to produce the GWO–XGBOOST–CEEMDAN model. Three carbon emission trading markets, Guangdong, Hubei, and Fujian, were experimentally predicted to verify the model's validity. Based on the experimental results, it has been demonstrated that the proposed hybrid model has enhanced prediction precision compared to the comparison model, providing an effective experimental method for the prediction of future carbon prices. [ABSTRACT FROM AUTHOR]

The development path from attaching importance to environmental protection to the theory of ecological conservation, then to piloting ecological civilization demonstration areas, marks that China’s ecological conservation has gradually moved from theoretical construction to practical exploration, based on the new idea that “lucid waters and lush mountains are invaluable assets.” It is still an ordeal for China’s ecological conservation in the context of global warming how to reduce carbon emission intensity while maintaining sustained economic growth. Under the dual constraints of peaking carbon dioxide emissions before 2030 and achieving carbon neutrality before 2060 (“dual carbon” goals), this paper employs the five national ecological civilization pilot demonstration areas (ECDAs) established in 2014 as quasi-natural experiments based on the panel data of 30 Chinese provincial regions from 2003 to 2019. Based on the analysis of the policy implementation background and the theoretical mechanism of its impact on carbon emission intensity, the synthetic control method and difference in differences method are adopted to test the impact of the construction of ECDAs on carbon emission intensity and examine the spatial spillover effect of the pilot policies. The study shows that the construction of ECDAs has significantly reduced carbon emission intensity as a whole, especially in Fujian, Guizhou, and Yunnan Provinces. Moreover, the conclusions successfully pass the robustness test. The mechanism analysis results demonstrate that the construction of ECDAs can lower carbon emission intensity through the positive incentives from boosting technological progress and developing green finance, and the reversal pressure mechanism of optimizing the energy structure and improving the market segmentation. The analysis results of the spatial spillover effect indicate that the construction of ECDAs plays a significant role in reducing carbon emission intensity in the region and its adjacent areas. Therefore, China should introduce ECDAs and the experience gained to more regions. Meanwhile, China should spare no effort to seek multi-dimensional paths to reduce carbon emissions in view of regional differences in green development, and strengthen cross-regional communications and cooperation to realize the goals of carbon emission reduction. [ABSTRACT FROM AUTHOR]

The severe global warming issue currently threatens humans' existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments' configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government's carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China's eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region's resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development. [ABSTRACT FROM AUTHOR]

In response to the challenges of global warming and the development of A low-carbon economy, the integrated electricity and natural gas energy system (IEGES) is known as an important structure for future energy supply; thus, its planning and design must take low-carbon and environmental protection factors into account. Regarding carbon emissions as an optimization criterion, this paper built life-cycle carbon emission models of IEGES components. Then, taking the capacities of the energy resources, storage and conversion units of IEGES as the optimization variables, a multi-objective optimization configuration model was established considering the annual investment operation cost and the life-cycle carbon emissions. The multi-objective model was transformed into a single-objective one by an ε-constraint approach and the polynomial fitting method was employed to obtain the value of ε for obtaining uniformly distributed Pareto sets. Based on the fuzzy entropy weight method and the fuzzy affiliation degree approach, the obtained Pareto sets were ranked and the solution with the highest ranking value was selected as the optimal solution for the original problem. Finally, the configuration schemes were analyzed from the perspectives of economy, carbon emission and renewable energy utilization, and the effectiveness and rationality of the proposed optimization method were verified through MATLAB simulation. [ABSTRACT FROM AUTHOR]

We model the welfare consequences of mandates that restrict investors to hold firms with net-zero carbon emissions. To qualify for these mandates, value-maximizing firms have to accumulate decarbonization capital. Qualification lowers a firm's required return by its decarbonization investments divided by Tobin's q, that is, the greenium or the dividend yield shareholders forgo to address the global-warming externality. The welfare-maximizing mandate approximates the first-best solution, yielding welfare gains compared to laissez-faire by mitigating the weather disaster risks resulting from carbon emissions. Our model generates optimal transition paths for decarbonization that we use to evaluate proposed net-zero targets. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online [ABSTRACT FROM AUTHOR]

As global warming becomes more prominent, the need to reduce carbon emissions to achieve China's carbon peak target is increasing. It is imperative to seek effective methods to predict carbon emissions and propose targeted emission reduction measures. In this paper, a comprehensive model integrating grey relational analysis (GRA), generalized regression neural network (GRNN) and fruit fly optimization algorithm (FOA) is constructed with carbon emission prediction as the research objective. Firstly, GRA is used for feature selection to find out the factors that have a strong influence on carbon emissions. Secondly, the parameter of GRNN is optimized using FOA algorithm to improve the prediction accuracy. The results show that (1) fossil energy consumption, population, urbanization rate and GDP are important factors affecting carbon emissions; (2) FOA-GRNN outperforms GRNN and back propagation neural network (BPNN), verifying the effectiveness of FOA-GRNN model for CO2 emission prediction. Finally, by analyzing the key influencing factors and combining scenario analysis with forecasting algorithms, the carbon emission trends in China for 2020-2035 are forecasted. The results can provide guidance for policy makers to set reasonable carbon emission reduction targets and adopt corresponding energy saving and emission reduction measures. [ABSTRACT FROM AUTHOR]

The problem of global warming has become a major global concern, and reducing greenhouse gas emissions is crucial to mitigate its effects. Photovoltaic power generation is clean, low-carbon energy. Photovoltaic products can convert solar energy into electricity, reducing CO2 emissions to an extent. This paper introduces the life cycle evaluation theory to assess the carbon emissions of photovoltaic curtain walls. PVsyst software allows for the simulation and calculation of power generation under different influencing factors, which provides valuable information about the carbon reduction potential of photovoltaic curtain walls. The evaluation of carbon emissions and their influencing factors using grey correlation analysis further enhances the understanding of the benefits and limitations of photovoltaic curtain walls. According to the results of grey correlation analysis, this paper concludes that the degree of various influencing factors on carbon emission of a photovoltaic curtain wall under different scenarios in descending order is as follows: orientation, location, inclination, shadow occlusion, and seasonal changes. The research findings of this paper provide a theoretical reference for the future development and application of photovoltaic curtain walls. By demonstrating the carbon reduction potential of this technology, this study contributes to promoting the adoption of photovoltaic curtain walls as a sustainable solution to mitigate the effects of global warming. [ABSTRACT FROM AUTHOR]

Refrigerant substitution is an urgent need in the context of reducing carbon emissions and slowing global warming. CO2 is now being proposed as a promising solution based on its excellent properties and system performance, especially in low-temperature environments. This paper presents an overview of recent advances in system configuration and operation characteristics to improve the performance of transcritical CO2 heat pump and refrigeration systems. The paper first introduces the basic research background, system cycle, and thermodynamic characteristics. Secondly, CO2 cycle improvements with single modifications and modification combinations are reviewed. Then, some important operation characteristics and control methods are discussed. Additionally, the paper provides a detailed description of the development of transcritical CO2 heat pump and refrigeration systems in the vehicle field. At the end of this review, conclusions and opportunities for future work in this field are presented. [ABSTRACT FROM AUTHOR]

Since global warming worsens with economic development and emitted CO2 is one of the main greenhouse gases, it is important to understand the relationship between CO2 emissions and economic growth. The paper applies a new panel cointegration test with cross-sectional dependence and structural breaks to examine this relationship in developed and developing countries, respectively. The results indicate that the "Environmental Kuznets Curve" does not hold in either group. For developing countries, there is neither linear nor quadratic long-term equilibrium relationship between CO2 emissions and economic growth. For developed countries, the quadratic relationship does exist between CO2 emissions and economic growth, whereas the linear one does not. A half of these countries have inverted U-shaped curves, while the other half have U-shaped curves. Besides, most of these countries are still on the rising stage of the curve. This paper gives new insights for policymakers to keep a balance between sustainable economic growth and suitable environmental quality. [ABSTRACT FROM AUTHOR]

There seems to be a consensus among economists in support of a carbon tax for addressing the costly implications of carbon dioxide emissions for the global climate. However, past international agreements on climate change instead specify caps on emissions (a quantity target) for each country. The aim of this paper is to explore several reasons why use of such quantity targets could dominate use of a carbon tax. For one, if a country were to impose a carbon tax at a rate high enough to correct for global externalities, this rate would far exceed the tax rate that would be in any given country's own self-interest. The result is a strong incentive to make use of a variety of other domestic government policies to encourage greater emissions, undercutting the intended abatement under a carbon tax. A quantity target instead by construction caps emissions. Second, the paper argues that a quantity target can better ensure that global warming remains below 2°C above preindustrial levels, given the uncertainties faced regarding the response to any given carbon-tax rate. Third, the set of quantity targets set for each country can more flexibly be adjusted to ensure that most all countries benefit from participating in a global accord while still allowing efficient patterns of abatement by giving countries credit for cross-border abatement efforts. [ABSTRACT FROM AUTHOR]

Nowadays environmental issues have been of great concern to the world, among which the problem of global warming caused by greenhouse gas emissions is particularly prominent. All countries in the Kyoto Protocol and the Paris Agreement have committed to control greenhouse gas emissions, and China, as the largest carbon emitter, has assumed a heavier burden. China has been striving to develop low-carbon technologies such as hydrogen, nuclear, wind, and solar energy, but the most attention should be paid to CCUS, which many scholars have high expectations that CCUS can help China reduce emissions to some extent. Therefore, this paper presents a prediction that CCUS can reduce 3.8% of carbon emissions for China in 2040 when CCUS emission reductions increase at a rate of 30%. The power and chemical industries could reduce carbon emissions by 2.3% and 17.3%, respectively. [ABSTRACT FROM AUTHOR]

Despite the serious threat of global warming caused by carbon emissions, commercial refrigeration systems that generate cooling are unable to adjust their usage time. Therefore, it is essential to enhance the energy efficiency of refrigeration systems themselves. Recently developed refrigeration systems offer improved energy consumption efficiency as they utilize inverter-type outdoor units that can communicate with indoor units. However, traditional legacy refrigeration systems still suffer from poor energy efficiency because the existing indoor units cannot communicate with inverter-type outdoor units. Hence, this paper introduces a heuristic energy efficiency improvement algorithm for commercial refrigeration systems using legacy indoor units when an inverter-type outdoor unit is installed. In particular, to reduce the computational complexity of the process of selecting the optimal temperature difference and target low-pressure variables that can enhance energy efficiency, the algorithm employs the Monte Carlo method. Finally, the performance of the proposed heuristic energy efficiency improvement algorithm was evaluated in a laboratory environment to confirm its applicability. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) is a known greenhouse gas and one of the largest contributors to global warming in the Earth's atmosphere. The remote detection and measurement of CO2 from industrial emissions are not routinely carried out and are typically calculated from the fuel combusted or measured directly within ducted vents. However, these methods are not applicable for the quantification of fugitive emissions of CO2. This work presents the results of remote measurement of CO2 emissions using the differential absorption lidar (DIAL) technique at a wavelength of ~2 µm. The results from the DIAL measurements compare well with simultaneous in-stack measurements, these datasets were plotted against each other and can be described by a linear regression of y (t/h) = 1.04 x − 0.02, suggesting any bias in the DIAL data is likely small. Moreover, using the definition outlined in EN 15267-3 a lower detection limit of 0.12 t/h was estimated for the 2 µm wavelength DIAL data, this is three orders of magnitude lower than the corresponding CO2 detection limit measured by NPL in the 1.5 µm wavelength region. Thus, this paper demonstrates the feasibility of high-resolution, ground-based DIAL measurements for quantifying industrial CO2 emissions. [ABSTRACT FROM AUTHOR]

This paper proposes a novel aviation CO 2 emissions reduction method by introducing a market-based incentive mechanism. An aviation carbon emissions prediction model is proposed to characterize both the microscopic flight dynamics and the macroscopic air traffic demand trend. Based on emissions prediction, a linear climate response model is applied to evaluate the environmental impact of the aviation carbon emissions. Finally, a market-based framework for aviation CO 2 emissions reduction is established based on a two-phase carbon trading model, which provides a piecewise charge method through emission taxation and cap-and-trade. A case study is carried out to demonstrate the effectiveness of the proposed framework using a real-world dataset from the Hefei Air Traffic Control Zone in China. Three scenarios are defined to describe the possible development speed of sustainable aviation fuel application in the future: not applied, medium-speed development, and high-speed development. The results show that the carbon peak will be reached in 2025 in the high-speed development scenario, when the emission amount and associated environmental impact will be reduced by 23.7% and 22.3%, respectively. Thus, the market-based framework proposed in this paper can effectively reduce aviation CO 2 emissions and mitigate the effects of global warming. • An aviation CO 2 emissions prediction model is proposed considering both microscopic flight dynamics and macroscopic traffic demand. • A more efficient market-based CO 2 emission reduction framework is developed based on a two-phase carbon trading model by means of taxation and cap-and-trade. • Case study shows that the proposed method has a more optimal incentive for CO 2 emission reduction. [ABSTRACT FROM AUTHOR]

Purpose of the study: The change of climate due to global warming has become a burning problem throughout the world. India is a country where global warming causes a concern to the scientists. Unrestricted emission of different green house gases mainly carbon dioxide is responsible for this alarming situation. This paper develops a state-wise nonlinear emission model of carbon dioxide, an important green house gas. Methodology: Non linear least-square and regression analysis method used to explain the emission of the gas. Main Findings: The short term and long-term forecast of carbon dioxide emission trend in the states are presented. Applications of this study: Model is used in the states of Gujarat, Maharashtra and Madhya Pradesh of India. Novelty/Originality of this study: Future prediction of emission of CO2 may be evaluated from our proposed model. This paper may be helpful for the future researchers for finding emission of CO2 and other GHGs for the remaining states in India. [ABSTRACT FROM AUTHOR]

In recent years, concurrent climate extreme conditions (i.e., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Specifically, to reduce the uncertainties from models, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to evaluate the impact of global warming on ultra‐extreme events over China. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. The frequency of extreme cold events is projected to be small. More frequent extreme hot‐wet events with concurrence in the same month and year would be expected for China under the continuously‐warming scenarios. This is particularly obvious for the west where more than 6 hot‐wet months are likely to take place under future warming scenarios. This may imply that more extreme heat waves and flooding events would coincide in the same month or year for China in the future. For univariate ultra‐extreme events, both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. When the global mean temperature is limited to 1.5°C rather than 2°C, the avoided impacts of hot‐wet and cold‐wet extremes concurring in the same month will be larger than those of dry‐related compound extremes. Overall, the results suggest that slowing down global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Plain Language Summary: In recent years, concurrent climate extreme conditions (e.g., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Here, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to address this question. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. Both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. Overall, the results suggest that slowing down the global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Key Points: A downscaled and bias‐corrected CMIP6 ensemble is used to evaluate the impact of global warming on ultra extreme events over ChinaMore frequent extreme hot‐wet events with concurrence in the same month and year would be expected for ChinaBoth extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level [ABSTRACT FROM AUTHOR]

This article explores the impact of high-standard farmland construction policy on green and low-carbon agricultural development, such policy has been ignored in literature from the perspective of policy evaluation and it is a promising cure for global warming. The effectiveness and impact mechanism of policy implementation are analyzed, it concludes that the policy significantly reduces agricultural carbon emissions by 12.3%, which benefits from the improvement of agricultural production efficiency. This paper opens a new window for policy-making for modern agriculture and the momentous challenge of the global warming. [ABSTRACT FROM AUTHOR]

The massive use of energy has caused a rapid increase in global carbon dioxide emissions, resulting in a series of environmental problems such as climate warming. Investment in the energy industry can guide funds into green and clean production, reduce carbon emissions in the energy industry, and promote the green development of the energy industry. This paper considers the energy, the environment, the economy, and other factors and focuses on energy consumption and investment structure. Taking 30 provinces in China as research samples, a dynamic spatial Durbin model is established. The results show that the first-order term of carbon emissions has a driving force of 0.5068% for current carbon emissions at a significance level of 1% and that the increase in current carbon emissions will lead to a continued increase in carbon emissions in the next period. The increase in the carbon emissions of neighbouring provinces will increase their carbon emissions through the spatial spillover effect. Whether in the short term or long term, the increase in energy investment and the optimization of the energy investment structure can reduce carbon emissions. The above conclusions can provide a reference for the formulation of government environmental policies. [ABSTRACT FROM AUTHOR]

Global warming is one of the most challenging issues of the current era. Revolutions in industrial, Information and Communication Technology (ICT) sectors significantly contribute in increasing global warming. Green House Gases (GHG) emissions from industrial, transportation, power and other sectors cause environmental pollution, which results in climate degradation. Environmental experts are well aware of the disastrous consequences of excessive global warming; therefore, several decarbonization strategies are developed and practiced in the recent past. A widely practiced decarbonization strategy is replacing fossil fuels by Renewable Energy Sources (RES) in the power systems. Electricity consumers are also encouraged to shift their consumption load to low carbon emissions' time periods. For accomplishing this task, an estimation of future carbon emissions is required. In this paper, power system's carbon emissions are predicted accurately with the help of a novel and an efficient forecasting model. The proposed model comprises of Spearman Correlation Analysis (SCA) and Improved Shallow Denoising Autoencoder (ISDAE) based feature engineering. Forecasting is performed through an Improved Particle Swarm Optimization (IPSO) based Deep Neural Network (DNN) forecaster. In addition, a comprehensive quantification analysis is also presented in this paper. The impacts of RES integration level on the electricity price, consumption cost and Greenhouse Gases (GHG) emissions are quantified descriptively and graphically. Performance of the proposed forecasting model is evaluated by Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE) and Mean Square Error (MSE). Simulation results prove that the proposed model outperforms Support Vector Machine (SVM) and Multiple Linear Regression (MLR) based carbon emission forecasting models in terms of forecasting accuracy. [ABSTRACT FROM AUTHOR]

Due to the emission of carbon dioxide and other greenhouse gases, the global climate is warming. As the world's biggest emitter of carbon emissions, China faces a more severe challenge in reducing carbon emissions than developed countries. A reasonable prediction of the carbon peak in China will help the government to formulate effective emission reduction paths. This paper analyzes the changes in carbon emissions in China from 2004 to 2020, uses the STIRPAT model and scenario analysis method to predict carbon emissions from 2021 to 2030, and then calculates the carbon efficiency during carbon peaking to select the most effective carbon peak path for China. The results show that China's carbon emissions increased year by year from 2004 to 2020. Under the baseline scenario, China is unlikely to reach its carbon peak before 2030. Under the regulatory scenarios, China can reach its carbon peak before 2030. The peak values from high to low are seen with the rapid development-weak carbon control scenario, rapid development-intensified carbon control scenario, slow development-weak carbon control scenario and slow development-intensified carbon control scenario, respectively. Correspondingly, China will peak its carbon emissions in 2029, 2028, 2028 and 2028, respectively, according to these scenarios. The carbon efficiency under the rapid development-weak carbon control scenario is the highest, which means that accelerating the growth rate of population, GDP and urbanization while moderately carrying out the transformation of industrial structure and energy structure is an effective way to achieve the goal of "carbon peak by 2030". [ABSTRACT FROM AUTHOR]

With the growth of global food demand, agricultural carbon emissions caused by agricultural production have become a major challenge in controlling global warming. However, a systematic and visual literature review of food security and carbon emissions (FSCE) is still lacking, and there is a lack of exploration on the balanced path between ensuring food security and realizing carbon emission reduction. Based on 872 articles related to FSCE in the Web of Science (WOS) core database, this paper used CiteSpace and VOSviewer bibliometric software to analyze the relevant research focus and trends. This study found that developed countries dominated the research in this field, and the quantity, quality, and intensity of their authors, institutions, and cooperation among countries are higher than those of developing countries. Although the intensity of interdisciplinary cooperation has increased, it remains at a low level. Since 2007, the number of papers published in this field has increased significantly, and the research perspectives have diversified. Moreover, the research theme continues to expand with the core of "food security," involving the impact of climate change, crop production and food security, soil carbon sequestration, and farmers' livelihood sustainability. In addition, food production, food transportation, and food loss reduction are key paths that need to be balanced to ensure global food security and realize carbon emission reduction, and how to promote "economic growth" under the constraints of FSCE will be a future research hotspot. [ABSTRACT FROM AUTHOR]

Climate change with global warming as the main feature associated with fossil energy use has been recognized as a threat to public health and welfare. Energy-related carbon emission reduction is a more serious challenge for BRI (Belt and Road Initiative) countries with rapid economic development. Examining key impact factors is necessary and helpful. This paper is the first study providing detailed country-by-country analyses aiming to identify the key drivers and inhibitors of energy-related carbon emission in 66 BRI countries with more systematic impact factors. The results show that: (1) Economic development (A), population (Ps), urbanization (Pu), and industrialization (Ss) are the key drivers for 52%, 26%, 11%, and 6% countries of BRI countries. Technological progress (T), energy consumption structure (E), and tertiary industry proportion (St) serve as key inhibitors for 65%, 17%, and 8% countries of BRI countries. (2) Different carbon emission reduction strategies should be formed on different geographical scales. At the international level, carbon emission reduction consensus should be reached and carbon emission reduction targets should be formulated. At the regional level of the Belt and Road Initiative, a carbon emission reduction cooperation fund should be established, and carbon emission reduction technologies and measures should be exchanged and data should be shared to promote the green development of the Belt and Road. At the national level, there should be carbon emission reduction policies reflecting national characteristics. At the local level, there should be specific carbon reduction measures in line with local conditions. [ABSTRACT FROM AUTHOR]

Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. Algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China's carbon dioxide emissions during the "14th Five-Year Plan" period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China's carbon dioxide emissions. During the "14th Five-Year Plan" period, China's carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly. [ABSTRACT FROM AUTHOR]

Global warming caused by increased carbon emissions is a common challenge for all mankind. Facing the unprecedented pressure of carbon emission reduction, it is particularly important to grasp the dynamics of carbon emission in time and accurately. This paper proposes a novel daily carbon emission forecasting model. Firstly, the daily carbon emission data is decomposed into a series of completely noise-free mode functions by improved complete ensemble empirical mode decomposition method with adaptive noise (ICEEMDAN). Then, a two-stage feature selection method composed of partial autocorrelation function (PACF) and ReliefF is applied to select appropriate input variables for the next prediction process. Finally, the extreme learning machine optimized by improved sparrow search algorithm (ISSA-ELM) is used to predict. The empirical results show that the proposed two-stage feature selection method can further improve the prediction accuracy. After two-stage feature selection, the values of R2, MAPE, and RMSE were improved by 0.55%, 30.23%, and 28.46%, respectively. It can also be found that ISSA has good optimization performance. By combining with ISSA, R2, MAPE, and RMSE improved by 7.60%, 31.97%, and 44.79%, respectively. Therefore, the proposed model can provide a valuable reference for the formulation of carbon emission reduction policies and future carbon emission prediction research. [ABSTRACT FROM AUTHOR]

In today's world, GHG emissions, especially CO2, drive rapid global warming. Construction significantly contributes to this by emitting CO2. Plants have long been recognized for their role in mitigating climate change through CO2 absorption, enhancing both climate control and environmental beauty. Thus, the aim of this paper is to assess plants' CO2 absorption potential, focusing on recent articles from reputable journals in the past decade. First, we delve into the primary causes of global warming. Next, we explore the philosophy of CO2 emissions in construction, from inception to completion. Finally, CO2 emission control through plantation is examined, exploring the potential of organic and genetically modified plants for real-world applications. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions and improving revenue in the face of global warming and economic challenges is a growing concern for airlines. This paper addresses the inefficiencies and high costs associated with current aero-engine on-wing washing strategies. To tackle this issue, we propose a reinforcement learning framework consisting of a Similar Sequence Method and a Taylor DQN model. The Similar Sequence Method, comprising a sample library, DTW algorithm, and boundary adjustment, predicts washed aero-engine data for the Taylor DQN model. Leveraging the proposed Taylor neural networks, our model outputs Q-values to make informed washing decisions using data from the Similar Sequence Method. Through simulations, we demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The urgent need for carbon‐neutral structures arises from the construction industry's contributions to global carbon emissions and the unprecedented rate of global warming. This paper focuses on steel tied‐arch bridges and explores the possibilities of achieving carbon‐neutral design. Firstly, a scientometric study is conducted to identify key focus areas and emerging research trends in this field. The study encompasses publications that emphasize minimizing either the cost of structures or their detrimental impact to the environment. Taking a steel tied‐arch bridge as an example, we subsequently examine its embodied carbon under different design and manufacturing factors. The study reveals that structural design modifications and the use of high‐strength steel can significantly reduce the global warming potential of the bridge's superstructure. The paper then discusses current methods to reduce (or even cancel out) the embodied carbon associated with the bridge's steel superstructure and concrete substructure. It is demonstrated that the tied‐arch bridge's total embodied carbon can be reduced to up to 82%. [ABSTRACT FROM AUTHOR]

Environmental impact awareness in civil engineering is nowadays an important concern. The last message from the IPCC (Intergovernmental Panel on Climate Change) was clear: significant actions are mandatory and urgent to achieve the objective to limit global warming to 1.5°C. The building sector is one of the most polluting industrial sectors for which economically viable solutions must be found to cut the emissions. The development of new production technologies can contribute to this aim by creating innovative and more sustainable materials. Amongst the new materials that appear on the steel market, high strength steels are a nice example as they offer higher strength to weight ratio of structural steel elements, resulting in material savings, lighter foundations, easier transportation, and erection. These multiple advantages explain why the use of high strength steels could lead to both significant carbon emission and cost savings. However, the production of such steels uses more alloying elements and sometimes they require more production energy than for regular grades. The aim of this paper is to estimate the relative prices and relative carbon emissions of high strength steels to evaluate whether they constitute sustainable materials. This paper demonstrates that, in many cases, the increase of relative prices and carbon emissions as function of yield strength can be negligible by contrast to the weight savings induced by using high strength steels. [ABSTRACT FROM AUTHOR]

China has paid considerable attention to developing and implementing a carbon labeling system in response to increasing environmental issues such as global warming. This paper aims to provide a theoretical basis for formulating and implementing a carbon labeling system in China through a study of consumer acceptance behavior and its influencing factors. This paper constructed an extended model of consumers' acceptance behavior of carbon neutral labels based on the theories and methods of Unified Theory of Acceptance and Use of Technology (UTAUT) and analyzed the effects of five factors (carbon label cognition, performance expectancy, effort expectancy, social influence, and facilitating factors) on consumer carbon neutral label acceptance and adoption. The structural equation model analysis revealed that carbon label cognition, performance expectancy, social influence, and facilitating factors significantly, and positively impact consumers' acceptance of carbon neutral labels. Moreover, carbon label cognition, performance expectancy, and facilitating factors significantly, and positively affect consumers' carbon neutral label adoption behavior. Meanwhile, carbon label cognition, and effort expectancy have no significant impact on consumers' willingness to accept carbon neutral labels, which in turn significantly impacts consumers' carbon neutral label adoption behavior. According to the research findings, increasing the promotion of carbon labeling and improving the practical strategies and management recommendations for carbon label design are proposed. [ABSTRACT FROM AUTHOR]

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Achieving carbon neutrality is an important initiative to cope with global warming. Most studies on the role of tourism towards carbon neutrality were based on the carbon emission reduction (CER) perspective while the carbon sink (CS) perspective was lacking. Based on the quasi-natural experiment of high-standard scenic spots, this paper aimed to examine the effect of tourism resources development on regional CS with high-precision multi-source datasets of 2601 Chinese county units. According to the results of the time-varying difference-in-difference (DID) model regression, CNSA significantly deteriorated CS, but the findings of the rolling regression test confirmed that the adverse effect diminished over time. [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]