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This study aimed to maximise the amount of CO2 storage by indirect carbonation using paper sludge ash (PSA), which is an alkaline industrial waste. The properties of the following three types of solvents were considered: acids (acetic acid and hydrochloric acid), ammonium salts (ammonium chloride and ammonium acetate) and others (sodium citrate and water). When carbon dioxide was injected into calcium leachates prepared from PSA using acids, carbonation did not occur, and using ammonium salts as solvent, the carbonation efficiency was low. However, after pH swing of the leachates using sodium hydroxide and ammonia solution for acid and ammonium salt solvents, up to 324 kg CO2/(ton PSA) and 175 kg CO2/(ton PSA) were stored, respectively. When carbon dioxide was injected into calcium leachates prepared from PSA using sodium citrate or water, solid was initially generated which later dissolved. In order to solve this problem, CO2 injection was stopped just before the pH of the solution reached 10.5 for sodium citrate and 8.3 for water, resulting in the storage of 136 kg CO2/(ton PSA) and 29 kg CO2/(ton PSA), respectively. Solids obtained through carbonation were pure calcium carbonate. [ABSTRACT FROM AUTHOR]

The paper reviews the existing applications of sensing technologies for measuring construction off-road vehicle emissions (COVE) such as earthmoving equipment. The current literature presented different measurement methods and reported the results of utilisation of new technologies for measuring COVE. However, previous papers used different technology applications covering only a part of the monitoring process with its own limitations. Since technologies are advancing and offering novel solutions, there is an urgent need to identify the gaps, re-evaluate the current methods, and develop a critical agenda for automating the entire process of collecting emissions data from construction sites, and monitoring the emission contributors across cities. This paper systematically identifies relevant papers through a search of three key databases—Web of Science, Engineering Valley and Scopus—covering the publications in the last decade from 2008 to 2017. An innovative robust research method was designed to select and analyse the relevant papers. The identified papers were stored in a data set, and a thematic algorithm employed to find the clusters of papers which might be potentially relevant. The selected papers were used for further micro-thematic analysis to find key relevant papers on COVE, and the gap in the literature. A sample of relevant papers was found relevant to COVE and critically reviewed by coding and content analysis. This paper critically reviews the selected papers and also shows that there is a considerable gap in the applications of new technologies for measuring in-use COVE in real time based on real activities toward automated methods. This review enables practitioners and scholars to gain a concrete understanding of the gap in measuring COVE and to provide a significant agenda for future technology applications. [ABSTRACT FROM AUTHOR]

The urgent need to address global carbon emissions and promote sustainable energy solutions has led to a growing interest in carbon dioxide (CO2) conversion technologies. Among these, the transformation of CO2 into methanol (MeOH) has gained prominence as an effective mitigation strategy. This review paper provides a comprehensive exploration of recent advances and applications in the direct utilization of CO2 for the synthesis of MeOH, encompassing various aspects from catalysts to market analysis, environmental impact, and future prospects. We begin by introducing the current state of CO2 mitigation strategies, highlighting the significance of carbon recycling through MeOH production. The paper delves into the chemistry and technology behind the conversion of CO2 into MeOH, encompassing key themes such as feedstock selection, material and energy supply, and the various conversion processes, including chemical, electrochemical, photochemical, and photoelectrochemical pathways. An in-depth analysis of heterogeneous and homogeneous catalysts for MeOH synthesis is provided, shedding light on the advantages and drawbacks of each. Furthermore, we explore diverse routes for CO2 hydrogenation into MeOH, emphasizing the technological advances and production processes associated with this sustainable transformation. As MeOH holds a pivotal role in a wide range of chemical applications and emerges as a promising transportation fuel, the paper explores its various chemical uses, transportation, storage, and distribution, as well as the evolving MeOH market. The environmental and energy implications of CO2 conversion to MeOH are discussed, including a thermodynamic analysis of the process and cost and energy evaluations for large-scale catalytic hydrogenation. [ABSTRACT FROM AUTHOR]

Using the new measure of the export quality of the International Monetary Fund (IMF), this paper investigates the effects of the product quality of exports on the growth rate of the per capita carbon dioxide emissions. The paper focuses on the panel dataset of 82 developing economies for the period from 1970 to 2014. Along with the index of export quality, we also consider the measures of the per capita income, per capita energy consumption, natural resource rents, and trade openness. The results indicate that there is the positive impact of the quality of exports on carbon dioxide emissions. There is also the positive relationship between the per capita income and carbon dioxide emissions. In addition, we find that the trade openness measures are positively related to carbon dioxide emissions. These results are robust to consider different income measures and to divide the developing economies, according to their income levels. [ABSTRACT FROM AUTHOR]

The surge of carbon dioxide emission plays a dominant role in global warming and climate change, posing an enormous threat to the development of human being and a profound impact on the global ecosystem. Thus, it is essential to analyze the carbon dioxide emission change trend through an accurate prediction to inform reasonable energy-saving emission reduction measures and effectively control the carbon dioxide emission from the source. This paper proposed a hybrid model by combining the random forest and extreme learning machine together for the carbon dioxide emission forecasting in this paper; the random forest is applied for influential factors analysis and the extreme learning machine for the prediction. To improve the performance of the prediction model, moth-flame optimization is adopted to optimize initial weight and bias in extreme learning machine. A case study whose data is derived from Hebei Province, China, during the period 1995-2015 is conducted to verify the effectiveness of the proposed model. Results show that the novel model outperforms the compared parallel models in carbon dioxide emission prediction and has the potential to improve the accuracy of CO2 emission forecasting. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to strengthen research concerning the impact of climate change on China's water resources, in order to avoid extreme weather events such as regional rainstorms and floods affecting the stable supply of energy and electricity. The goal is to reorganize energy industry allocation to make China's energy the most efficient, which would thus help achieve sustainable development goals. The biggest contribution and of this paper is not only to enlarge the scale and take into account the sustainable aspects, but also to consider climate change factors that have been ignored in the past as exogenous variables. For the first time, we take stock of the optimal allocation of new and tradition energy sources and carbon dioxide. This study uses the Dynamic ZSG-DDF model with exogenous variables and undesirable outputs to find a way to redistribute carbon dioxide, new energy, and tradition energy from different provinces on the premise of maximizing efficiency, thus achieving the goal of reducing carbon and saving the earth. The research results show that the energy efficiency of tradition energy is higher than that of new energy in various provinces. At the same time, the China government must comprehensively review the quota targets of new energy, tradition energy, and carbon dioxide (CO2) in each province as quickly as possible. Currently, the quotas of various provinces are seriously misplaced, and 26 provinces in total have great room for improvement. [ABSTRACT FROM AUTHOR]

In recent decades, many authors have investigated possibility of simultaneous reduction of income inequality and pollution related to climate change. However, none of the previous researches reviewed the empirical studies of relationship between inequality and CO2 emissions. The authors of the selected papers, published from 2001 to 2019, found a diverse impact of income inequality on carbon dioxide (CO2) emissions. It could depend on different trajectories of Kuznets curves. Furthermore, the majority of authors have provided theoretical analysis (even including human behaviour) of the influence of income inequality on CO2 emissions, considering only territorial (or production-based) emissions. However, this paper recommends to distinguish between the production-based and consumption-based emissions and suggests the revised approaches to the impact of income inequality on CO2 emissions with regard to the production-based and consumption-based emissions. Regarding the production-based emissions, the following approaches are proposed: (i) determination of environmental policies, which also could reveal and explain the leakage phenomenon, and the channels of EKC, i.e. (ii) scale, (iii) composition, (iv) technical and (v) globalization effects. The influence of income inequality on consumption-based emissions can be explained by applying two approaches, namely (i) inequality determined changes in working time (this approach is also related to "Veblen effect") and (ii) individual economic behaviours of households. Therefore, the present study provides a new insight into the subject of the relationship between income inequality and pollution related to climate change. [ABSTRACT FROM AUTHOR]

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

Collaborative disposal of sludge has been applied extensively in cement industry kilns, but the influence of organic components in sludge on the selective non-catalytic reduction (SNCR) denitrification is still in highly demanding. This paper focuses the influence of ethanol, n-octane, benzene, and acetone on the SNCR denitrification, and the influence of ethanol are analyzed on SNCR denitrification under different flue gas condition. The result showed that the presence of organic components reduces the reaction temperature of SNCR. When the temperature of SNCR reaction is at 750 ~ 800 ℃, the denitrification rate of four organic components is ethanol > benzene > n-octane > acetone. The optimum denitrification rate on SNCR can reach as high as 90.377% when the reaction condition is the NH3/NO is 1; the ethanol concentration is 1000 ppm; O2 concentration is 2%; and temperature is 750 ℃. The presence of carbon dioxide inhibits the denitrification reaction. With the increase of temperature, the influence of SO2 on the denitrification reaction first increases and then decreases. In this paper, the influence of organic components in sludge on SNCR denitrification will be analyzed, which provides a theoretical basis for the selection of sludge dosing method for cement kiln collaborative disposal. [ABSTRACT FROM AUTHOR]

The development and renewal of gas sensor technology have enabled more and more low-cost gas sensors to form a carbon monitoring network to meet the requirements of the city. In the context of China's commitment to achieving the "double carbon" target by 2060, this paper reviews the principles of four standard gas sensors and the application of several low-cost sensors in urban carbon monitoring networks, with the aim of providing a practical reference for the future deployment of carbon monitoring networks in Chinese cities. Moreover, the types, prices, and deployment of the sensors used in each project are summarized. Based on this review, non-dispersive infrared sensors have the best performance among the sensors and are commonly used in many cities. Lots of urban climate networks in cities were summarized by many reviews in the literature, but only a few sensors were studied, and they did not consider carbon dioxide (CO2) sensors. This review focuses on the dense CO2 urban monitoring network, and some case studies are also discussed, such as Seoul and San Francisco. To address the issue of how to better ensure the balance between cost and accuracy in the deployment of sensor networks, this paper proposes a method of simultaneously deploying medium-precision and high-precision fixed sensors and mobile sensors to form an urban carbon monitoring network. Finally, the prospects and recommendations, such as different ways to mitigate CO2 and develop an entire carbon monitoring system for future urban carbon monitoring in China, are also presented. [ABSTRACT FROM AUTHOR]

Spatiotemporal variations of industrial carbon emissions (IE) must be scientifically understood, which will be helpful to formulate reasonable emission reduction strategies. Given that spatial distribution of IE is irrelevant to space agents commonly used (such as population and nighttime light), estimation and spatialization methods for total carbon dioxide (CO2) emissions are not entirely suitable for IE. Therefore, this paper used greenhouse gases observing satellite level 4A product to estimate IE at the city level and used industrial land density to obtain the distribution of IE within the administrative districts. Sectoral emission inventories of 182 cities and a mosaic Asian anthropogenic emission inventory named MIX were used to verify the results. Then, spatiotemporal variation characteristics of China's IE were analyzed from multiple levels. Results showed that (1) the mean relative error of estimation results was 56.11%, among which 62 cities had relative error of less than 30%. Gridded IE in this paper had high consistency with MIX. (2) Cities with high IE experienced rapid growth from 2009 to 2012, followed by slower growth from 2012 to 2017. (3) Centroid of significant cold and hot spots moved to the southeast and northwest, respectively. Most cities with high annual IE growth had relatively low emission efficiency, mainly located in Inner Mongolia and Xinjiang. Aggregation of medium and high IE grids may represent high emission efficiency. Significant differences still exist between cities in IE, and sustainable development strategies should be formulated according to local conditions. Regions with high annual growth or low emission efficiency are the key to achieving IE reduction targets in future. [ABSTRACT FROM AUTHOR]

Half of the world population live in the cities. Cities energy consumption, environmental impact, and the opportunities they provide for our planet's sustainability make them attractive for governmental authorities. Any action taken in the cities has immediate repercussions. For this reason, many statistical data are published every year. This paper makes the best use of these data to calculate cities CO2 emissions and their thermal and electric energy consumption. The methodology applied takes into consideration each city size by number of inhabitants and gets results per inhabitant and household. This will make possible to put into practice the right actions to reduce CO2 emissions and to use alternative energy. This paper also defines an index to facilitate and simplify the analysis of results. This study was applied to the case of Spain to show the methodology here proposed. In fact, this type of study has never been carried out in Spain before. With this purpose, the 145 Spanish cities with more than 50,000 people were considered. Results show that cities with larger populations present higher consumptions per inhabitant and household. The smallest the population of a city is, the less energy the city consumes. However, electric energy consumption remains constant regardless of the population size. With regard to the CO2 emissions, results bring to light that the biggest cities produce the highest emissions. Furthermore, comparing emissions produced by electrical sources to the total emissions, it was concluded that the smallest cities produce the highest electrical emissions. [ABSTRACT FROM AUTHOR]

China is the largest carbon dioxide emitter in the world. Over the past decades, China has made great efforts to retard the carbon emission growth. This paper aims to quantify the driving forces of stagewise provincial carbon emission changes and expound the bottom-up provincial efforts aligned with China's carbon abatement targets. During 2007–2012, economic growth as the major contributing factor posed a great increase in provincial carbon emissions, especially in underdeveloped provinces such as Inner Mongolia, Anhui, Guangxi, Shaanxi, and Ningxia. During 2012–2017, the energy intensity effect replaced economic growth as the largest contributor to provincial carbon emission changes. In both two periods, industrial structure upgrading and renewable energy expansion are conducive to mitigate provincial carbon growth, and the power sector will continue to contribute to national decarbonization. Lastly, this paper provides insight into national and provincial carbon emission abatement. [ABSTRACT FROM AUTHOR]

This paper employs a recently constructed consumption-based carbon dioxide emissions data in which emissions computations are made based on fossil fuel usage domestically, in addition to emissions emanating from imports minus exports. We contrast this measure with the commonly measured territory-based carbon dioxide emissions data and examine how trade performance (split into imports, exports, and total trade) impacts these two measures of carbon dioxide. We focus on 22 sub-Saharan African countries over the period 1995–2014. Employing the system generalized method of moments, we find trade to generally have positive effect on emissions. The results are consistent across the different measures of trade and carbon dioxide emissions. The results of the paper allow us to give some policy suggestions regarding carbon dioxide emissions in sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

Organosiloxanes are industrially produced worldwide in millions of tons per annum and are widely used by industry, professionals, and consumers. Some of these compounds are PBT (persistent, biaccumulative and toxic) or vPvB (very persistent and very bioaccumulative). If organosiloxanes react at all in the environment, Si–O bonds are hydrolyzed or Si–C bonds are oxidatively cleaved, to result finally in silica and carbon dioxide. In strong contrast and very unexpectedly, recently formation of new Si–CH3 bonds from siloxanes and methane by the action of microorganisms under mild ambient conditions was proposed (in landfills or digesters) and even reported (in a biotrickling filter, 30 °C). This is very surprising in view of the harsh conditions required in industrial Si–CH3 synthesis. Here, we scrutinized the pertinent papers, with the result that evidence put forward for Si–C bond formation from siloxanes and methane in technical microbiological systems is invalid, suggesting such reactions will not occur in the environment where they are even less favored by conditions. The claim of such reactions followed from erroneous calculations and misinterpretation of experimental results. We propose an alternative explanation of the experimental observations, i.e., the putative observation of such reactions was presumably due to confusion of two compounds, hexamethyldisiloxane and dimethylsilanediol, that elute at similar retention times from standard GC columns. [ABSTRACT FROM AUTHOR]

In this paper, we report Na3BiO4-Bi2O3 mixed oxide nanoplates for carbon dioxide gas sensing applications. These nanoplates have been synthesized using electrochemical deposition with potentiostatic mode on ITO substrate and characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD) to analyze their surface morphology and structure. SEM study shows the presence of horizontally aligned nanoplates stacked on top of one another (thickness ≈ 40 to 75 nm). XRD pattern shows the presence of monoclinic Na3BiO4 and Bi2O3. The gas percentage response is evaluated by measuring the change in electrical resistance of the nanoplates in the presence of carbon dioxide for different pressures at 50 °C, 75 °C, and 100 °C. Percentage response of more than 100% is seen at 30 psi gas pressure which increases to ≈ 277% at 90 psi at 100◦C. [ABSTRACT FROM AUTHOR]

With the increase of carbon dioxide concentration in the atmosphere, the living environment of human beings is seriously affected. As a high carbon emission industry in China, thermal power enterprises are the key areas of carbon emission reduction in China. This paper first uses Super-SBM model to measure the performance of China's 18 major thermal power enterprises in 2009–2018 from a static point of view. After considering the carbon emission constraints, it analyzes the degree of change in enterprise performance, and finds that the impact of carbon emission constraints on enterprise performance is not absolute. After that, with the help of Malmquist index model, this paper discusses the dynamic changes of thermal power enterprises' performance under carbon emission constraint in recent 10 yrs. The results show that the overall performance of carbon emission constraint is in a weak regression stage and summarizes the disadvantages of different enterprises. On the basis of the research conclusion, this paper puts forward countermeasures and suggestions to further improve the performance of Chinese thermal power enterprises under the carbon emission constraints in the future, which is conducive to different enterprises to optimize their own disadvantages. [ABSTRACT FROM AUTHOR]

Nitrogen-rich organic polymer poly(chloride triazole) (PCTs) was synthesized by a one-step method as metal-halogen-free heterogeneous catalyst for the solvent-free CO2 cycloaddition. PCTs had abundant nitrogen sites and hydrogen bond donors, exhibited great activity for the cycloaddition of CO2 and epichlorohydrin, and achieved 99.6% yield of chloropropene carbonate under the conditions of 110 ℃, 6 h, and 0.5 MPa CO2. The activation of epoxides and CO2 by hydrogen bond donor and nitrogen sites was further explained by density functional theory (DFT) calculations. In summary, this study showed that nitrogen-rich organic polymer is a versatile platform for CO2 cycloaddition, and this paper provides a reference for the design of CO2 cycloaddition catalysts. [ABSTRACT FROM AUTHOR]

The emission of greenhouse gases, especially carbon dioxide, accelerates global warming and poses a crisis to the environment and human society. The carbon emission of products is mainly determined by the design stage of the life cycle. However, the data in the scheme design stage has certain fuzziness and uncertainty. Therefore, it is difficult to calculate the carbon footprint directly. In this paper, a carbon footprint prediction model of linkage mechanism scheme design stage (CFPL-SDS) is proposed to help designers make decisions. Firstly, the CFPL-SDS is built to quantify the carbon performance of linkage mechanism. Secondly, according to the structural characteristics of the closed-loop cascade rehabilitation robot, a four-finger training mechanism is designed. Finally, the model is applied to the four-finger training mechanism to verify its feasibility. The results show that the CFPL-SDS can calculate the carbon footprint of the linkage in the design stage. Moreover, the CFPL-SDS establishes the mathematical model foundation for the low-carbon optimization problem of linkage mechanism. [ABSTRACT FROM AUTHOR]

Understanding the effect of freight structure (FS) on carbon dioxide (CO2) emissions is significant for policymakers in the transport sector. However, few studies have investigated fully how the freight structure influences transport CO2 emissions. In the paper, a comprehensive freight structure index is proposed to assess the freight structure in 16 provinces of China during 2005–2019. Then, the heterogeneous effect of the freight structure and other factors on CO2 emissions are quantified by the quantile regression model developed. The mediating effect between freight structure and CO2 emissions is further examined. And the panel causality test is used to explore the directions of causality among variables. The main results indicate that (1) the level of freight structure shows a distribution of increase from western and central regions to eastern regions in 16 provinces. (2) Freight structure has a positive effect on transport CO2 emissions, and its effect is significantly heterogeneous in all quantiles. (3) Freight structure has an indirect impact on CO2 emissions through the scale effect. (4) The bidirectional causality is confirmed between economic growth, industrial structure, and information and communication technology with carbon emissions. A unidirectional causality from CO2 emissions to freight structure is also found. Finally, some policy recommendations are proposed for sustainable transport development in China. The results facilitate the formulation of the regionally tailored carbon emission reduction strategy in the transport sector. [ABSTRACT FROM AUTHOR]

This paper re-estimates the environmental Kuznets curve (EKC) in China. To this end, it uses the unit root tests with structural breaks and the autoregressive-distributed lag (ARDL) estimations over the period 1971-2010. The special role is given to the impact of export product quality on CO emissions in the empirical models. The paper finds that the EKC hypothesis is applicable in China. It also observes the positive effect from energy consumption to CO emissions. In addition, it finds that the export product quality is negatively associated with CO emissions. The paper also argues potential implications. [ABSTRACT FROM AUTHOR]

Recognizing the process and identifying the drivers of energy-related CO2 emissions can provide suggestions for designing carbon emission reduction paths, and then promote the further reduction of carbon emission. In this paper, the carbon emissions in China and its subordinate provinces during 2005–2017 are firstly divided into four stages, named S1, S2, S3, and S4. The results show that China has just entered the S3, and it is impossible to reach the peak of energy-related CO2 emissions with steady economic growth before 2030. Then, three-layer LMDI is utilized to explore the drivers of CO2 emissions, and the impact of urbanization which is separated from the population is considered innovatively. The economic development increases CO2 emissions, while the other drivers have diverse effects, which may be positive or negative, on carbon emissions in different regions. Therefore, four emission reduction paths with provincial characteristics should be followed in the future: (i) three provinces, namely, Ningxia, Shaanxi, and Xinjiang, should optimize multiple basic objectives in parallel; (ii) four provinces, such as Inner Mongolia and Hainan, should optimize the energy structure; (iii) six provinces, such as Jiangxi and Hunan, should optimize the industry structure; and (iv) the other provinces should develop new clean energy according to regional conditions. [ABSTRACT FROM AUTHOR]

This study examines the relationship between FDI and CO2 emissions in a nonlinear framework using data from countries along the "One Belt, One Road" from 1979 to 2017 as the sample. First, the linear analysis method was used to examine the stability of FDI and CO2 emissions in countries along the "One Belt, One Road." In addition, the BDS (Brock-Dechert-Scheinkman test) method and nonlinear Granger causality test are used to investigate the nonlinear relationship between variables. Finally, a threshold vector autoregressive model (TVAR) is used to analyze the dynamic change mechanism between China's FDI and CO2 emissions, and a threshold vector error correction model (TVECM) is used to test how the variables respond to deviations from equilibrium. Then, the Markov switching model is used to test the robustness of the results. The research results show that China, India, South Africa, and other countries all have a nonlinear causal relationship between FDI and CO2 emissions. At the same time, the comovement of FDI and CO2 emissions in China has obvious structural break features, which are relevant for the underlying regime. Furthermore, the results also show that the adjustment process of the FDI toward equilibrium is highly persistent in the first regime, and CO2 emissions will adjust to an equilibrium state at a faster speed in the second regime. Therefore, this paper puts forward different policy suggestions for different countries. For China, we should pay attention to the long-term benefits of FDI and introduce high-tech green FDI. [ABSTRACT FROM AUTHOR]

The concentration of carbon dioxide gas has accelerated over the last two decades which cause drastic changes in the climatic conditions. In industries, carbon capture plants use a volatile organic solvent which causes many environmental threats. So, a low-cost green absorbent has been formulated with nontoxicity and high selectivity properties for absorbing carbon dioxide gas. This paper contains the synthesis process along with the structure confirmation using 1H NMR, 13C NMR, FT-IR, and mass spectroscopy. Density, viscosity, and diffusivity are measured at different ranges with standard instruments. The kinetic studies were also conducted in a standard predefined-interface stirred cell reactor. The kinetic parameters were calculated at different parameters like agitation speeds, absorption temperature, initial concentrations of ionic liquid, and partial pressure of carbon dioxide. The reaction regime of carbon dioxide absorption is found to be in fast reaction kinetics with pseudo-first-order. The reaction rate and the activation energy of CO2 absorption are experimentally determined in the range of 299 to 333 K with different initial concentrations of ionic liquid (0.1–1.1 kmol/m3). The second-order rate constant and activation energy of carbon dioxide absorption in the synthesized ionic liquid is found to be 9.48 × 103 m3 mol−1 s−1 and 16.61 kJ mol−1 respectively. On increasing the viscosity of the reacting solvent, the diffusivity of CO2 gas molecules decreases, and thus the rate of absorption decreases. This solvent has shown great potential to absorb CO2 at a large scale. [ABSTRACT FROM AUTHOR]

Environmental degradation is one of the most important and vital issues of today. In this context, many researchers are testing the environmental impact of different indicators. Many economic parameters affect environmental degradation. At the forefront of these parameters is the productive economic structures of the countries. For the first time in the literature, the present paper discusses the dynamic relationship between carbon dioxide (CO2) emissions, economic growth, and the productive capacity index (PCI) for a panel of 38 organization for economic co-operation and development (OECD) countries spanning the period 2000–2018. In this context, the PCI serves as a measure of the productive economic structure of a country. This empirical study applies panel cointegration techniques to reveal that the series are cointegrated in the long-run. In addition, the pooled mean group-panel autoregressive distributive lag (PMG-ARDL) approach is employed to estimate long-run coefficients. These coefficients confirm the environmental Kuznets curve hypothesis. Finally, the empirical findings confirm that improved productive capacity decreases environmental degradation. This results in important policy recommendations for involved governmental and private stakeholders. [ABSTRACT FROM AUTHOR]

This paper investigates whether the carbon dioxide (CO2) intensity of GDP matters for environmental degradation in an emerging country, namely, Turkey, over the period of 1990–2018 while controlling economic growth, foreign direct investment, and renewable energy consumption. The present study uses both linear and nonlinear time series estimators, namely, the Gregory and Hansen cointegration test, bounds test, nonlinear autoregressive distributed lag (NARDL) model, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegrating regressions (CCR), to encapsulate the possible effect of CO2 intensity of GDP, economic growth, foreign direct investment, and renewable energy consumption on environmental degradation in Turkey. The empirical finding of the present study reveals that the CO2 intensity of GDP is an important factor to determine environmental degradation in Turkey and the declining CO2 intensity of GDP reduces environmental degradation. Moreover, economic growth is the primary environmental sustainability factor in Turkey. The result is vital for policymaking and can perhaps be applied to take decisive policy actions to mitigate environmental issues. [ABSTRACT FROM AUTHOR]

The aim of this paper is to augment the existing literature on convergence of CO2 emissions, by adding carbon footprint per capita and ecological footprint per capita to the convergence debate. We use the residual augmented least squares regression to examine the stochastic convergence of the environmental indices in 27 OECD countries. Furthermore, in contrast to the previous studies which mainly used the conventional beta-convergence approach to examine conditional convergence, we use a beta-convergence method that is capable of identifying the actual number of countries that contribute to conditional convergence. The sigma-convergence of the environmental indices is also examined. The results suggest that conditional convergence exists in 12 countries for CO2 emissions per capita, 15 countries for carbon footprint per capita and also 13 countries for ecological footprint per capita. There is evidence for sigma-convergence for all the three indicators. The policy implications of the results are discussed in the body of the paper. [ABSTRACT FROM AUTHOR]

The paper aims at evaluating the nonlinear and complex relations between CO2 emissions, economic development, and petrol prices to obtain new insights regarding the shape of the environmental Kuznets curve (EKC) in the USA and in the UK in addition to introducing a newly proposed nonlinear approach. Within this respect, the paper has three purposes: the first one is to combine the multilayer perceptron neural networks (MLP) with Markov-switching vector autoregressive (MS-VAR) type nonlinear models to obtain the MS-VAR-MLP model. The second is to utilize one of the largest datasets in the literature covering the 1871-2016 period, a long span of data starting from the late eighteenth century. Since the emission, economic development, and petrol price relation is subject to nonlinearity and trajectory changes due to many historical events, the development of the MS-VAR-MLP model is a necessity to contribute to the ongoing debate regarding the shape of the EKC curve and the stability of the relation. The third purpose is to develop the MS-VAR-MLP-based regime-dependent sensitivity analysis, which eases the visual interpretation of the nonlinear causal relationships, which are allowed to have asymmetric interactions in different phases of the expansionary and recessionary periods of the business cycles. Our results provide clear deviations from the findings in the literature: (i) the shape of the EKC curve cannot be assumed to be stable and is subject to regime dependency, nonlinearity, and magnitude dependency; (ii) the forecast results suggest that incorporation of regime switching and neural networks provide significant improvement over the MS-VAR counterpart; and (iii) for both USA and UK and for the 1871-2016 period, the positive impacts of economic growth on emissions cannot be rejected for the majority of the phases of the business cycles; however, the magnitude of this effect is at various degrees. In addition, the incorporation of petrol price provides significant findings considering its effects on emission and economic growth rates. The analysis suggest clear deviations from the expected shape of the EKC curve and puts forth the necessity to utilize more complex empirical methodologies to evaluate the EKC since the emissions-economic development relation is more complex than it was assumed. Following these findings, several policy recommendations are provided. Lastly, the proposed MS-VAR-MLP methodology is compared with the MS-VAR model and various advantages and disadvantages are enumerated. [ABSTRACT FROM AUTHOR]

This paper investigates the impacts of new digital technology on carbon emissions in China. To better capture the feature of new digital technology, we distinctively measure it using the invention patent stock of new generation information technology industry. The empirical results show that the development of new digital technology significantly reduces carbon dioxide (CO2) intensity. The results are still robust after addressing the endogeneity issues using instrumental variable regressions. The carbon reduction mechanisms of new digital technology include reducing energy intensity, optimizing energy structure, and advancing treatment technology. We further confirm the spatial spillover and nonlinear effects of new digital technology on CO2 intensity and the moderating effect of urban digitalization in carbon reduction. The study suggests that the potential of new digital technology in reducing carbon emissions should be highly attentioned by governments. [ABSTRACT FROM AUTHOR]

By likening biocapacity and ecological footprint, the load capacity factor follows a specified ecological threshold, permitting for an in-depth analysis of ecological damage. It can be seen that as the load capacity factor is reduced, the ecological damage intensifies. Until now, scholars have used carbon dioxide, ecological footprint, nitrogen oxide, sulfur dioxide, and other indices to objectively examine ecological problems. The utilization of these metrics can cause the supply side of ecological concerns to be overlooked. To make up for this weakness, this paper evaluates the impact of structural change and trade globalization on the load capacity factor. The research also considers other drivers of load capacity factors such as economic growth and energy. We utilized the nonparametric such as nonparametric causality and quantile-on-quantile (QQ) regression approaches to scrutinize these interconnections for South Korea between 1970 and 2018. The findings from the QQ approach disclosed that in the majority of the quantiles, the influence of economic growth, structural change, energies (renewable and nonrenewable), and trade globalization mitigate the load capacity factor. Moreover, the nonparametric causality test divulged that in variance and mean, all the independent variables can predict the load capacity factor. Policy proposals for South Korea's sustainable development are offered based on the findings. [ABSTRACT FROM AUTHOR]

Environmental global issues affecting global warming, such as carbon dioxide (CO2), have attracted the attention of researchers around the world. This paper reviews and discusses the ground improvement and its contribution to reducing CO2 in the atmosphere. The approach is divided into three parts: the Streamlined Energy and Emissions Assessment Model (SEEAM), the replacement of soil stabilisation materials that lead to the emission of a large amount of CO2 with alternatives and mineral carbonation. A brief discussion about the first two is reviewed in this paper and a detailed discussion about mineral carbonation and its role in enhancing soil strength while absorbing a large amount of CO2. It is emphasised that natural mineral carbonation requires a very long time for a material to reach its full capacity to form CO2; as a result, different acceleration processes can be done from increasing pressure, temperature, the concentration of CO2 and the addition of various additives. In conclusion, it was found that magnesium is more attractive than calcium, and calcium is complicated in terms of strength behaviour. Magnesium has a larger capacity for CO2 sequestration and it has a greater potential to increase soil strength than calcium. [ABSTRACT FROM AUTHOR]

Given that Turkey has recently committed itself for the first time to reducing its CO2 emissions in the interest of sustainable growth in not only Turkey but also the world as a whole, this paper examines the relationship between energy consumption, CO2 emissions, and economic growth in Turkey for the period 1960–2014. In view of the different findings concerning causality and the character of the relationships between these variables revealed in our review of past studies (in most cases using quite different methods), this paper utilizes several different but related methodological approaches for identifying causal relationships. These include both the Toda and Yamamoto (1995) approach, the Fourier Toda-Yamamoto for Cumulative Frequency approach developed by Nazlioglu et al. (2016), vector error correction model (VECM) methodology, and the asymmetric Granger causality test proposed by Hatemi-J (Empir Econ 43:447–456, Hatemi-j 2012). Our results show that, when we apply the popular Toda-Yamamoto model, causality in these relationships is not confirmed even among any of the relevant variables in Turkey. Yet, when the Fourier Toda-Yamamoto tests for cumulative frequency are employed, we find unidirectional causality running from GDP per capita to emissions of CO2 per capita. Moreover, when we utilize the VECM methodology, the results show that long-run causality exists from GDP per capita and energy to CO2 emissions. When we apply the asymmetric causality tests, the results provide even stronger evidence for a unidirectional causal relationship from GDP per capita to CO2 emissions. As a result, the latter sets of results, based on more realistic conditions, suggest very strongly that, if Turkey is to meet the objectives of its ambitious Climate Change Action Plan commitment to the United Nations to reduce its CO2 per capita emissions relative to its past trends by up to 21% over the coming 2021–2030 decade, it is going to get very serious about the best way to do this as soon as possible. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) is the most prevalent greenhouse gas that triggers climate change, which in turn leads to catastrophic effects on trade, business, human health, and other areas. Understanding the characteristics and tendency of CO2 emissions will improve policy making and mitigation strategies. Understanding the linearity or nonlinearity and convergence or divergence of CO2 emissions is essential for selecting appropriate modeling techniques and for designing reliable policies. Therefore, this paper investigates the nonlinearity and nonlinear convergence of CO2 emissions among the world's top 20 highest emitting countries, which account for 80% of the world's total emissions. To check the nonlinearity of CO2 emissions, the McLeod–Li nonlinearity test, the Terasvirta nonlinearity test, and the Brock–Dechert–Scheinkman–LeBaron nonlinearity test are employed. The convergence or divergence of CO2 emissions is checked by using the Kilic nonlinear unit root test, the Hu and Chen nonlinear unit root test, and the Park and Shintani nonlinear unit root test. The findings revealed that the CO2 emissions process in all the 20 countries is nonlinear; 17 countries exhibit convergence in CO2 emissions while the other 3 countries diverged from 1960 to 2018. Based on the results, the nonlinear nature of CO2 emissions requires special attention from scholars when selecting estimation techniques for CO2 emissions. For countries with convergence, emissions trends can be used to forecast future values of CO2 emissions. Moreover, strong policy actions are required to achieve convergence in the countries with divergence. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the links between renewable energy (RE), non-renewable energy (NRE), capital, labour and economic growth, using the nonlinear autoregressive distributive lag (NARDL) model in Malaysia for the period of 1980–2018. The results of NARDL confirm the asymmetric effect of RE and NRE consumption on the economic growth in the long run as well as the short run in Malaysia. The findings also show that in the long and short run, positive shocks of NRE are greater than the positive shocks of RE. It indicates that Malaysia's economic growth is highly dependent on NRE which is not a good indication as NRE consumption increases carbon dioxide (CO2) emission in the country. Moreover, the empirical results of this study demonstrated that RE consumption reduction accelerates economic growth, whereas NRE consumption reduction decreases economic growth. It can have claimed that in Malaysia, RE is still more expensive than NRE. In conclusion, this study offered a variety of measures to develop RE to reduce the dependency on NRE consumption. [ABSTRACT FROM AUTHOR]

This paper applies the modified undesirable dynamic data envelopment analysis (DEA) model by considering the role of forest carbon sinks to evaluate European countries' carbon dioxide (CO2) emissions and productivity efficiency. Taking population and energy consumption as input variables, gross domestic product (GDP) as the desirable input, CO2 as the undesirable output, and fixed assets as an inter-temporal carry-out input variable, our results suggest considering the fixed amount of the forest carbon sinks significantly affects efficiency rankings. The overall efficiency rankings for Ireland, Austria, Italy, Germany, Spain, and Belgium look to be overrated, while those of Finland, France, and Netherlands are apparently underrated. In terms of Total-Factor Efficiency analysis, countries with the best performance in efficiency ranking are Denmark, Luxembourg, Norway, Sweden, and the UK, thanks to their long-term effort at addressing the impact of forest carbon sinks and the effect of CO2 emissions on efficiency. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between greenhouse gas emissions, energy consumption, and output growth among African OPEC countries (Libya, Nigeria, Angola, Algeria, Equatorial Guinea, and Gabon) using the panel autoregressive distributed lag model (PARDL) estimated by means of mean group (MG) and pooled mean group (PMG) for the period 1970–2016. The paper estimated three panel models comprising the components of greenhouse gasses which includes nitrous oxide, carbon dioxide (CO2), and methane and examined their relationship with economic growth and energy consumption. The findings of the study showed evidence of a positive impact of economic growth on both CO2 and methane emissions in the long run. Its impact on nitrous oxide emissions although positive was found to be statistically insignificant. Energy consumption was also found to produce an insignificant positive impact on CO2, methane, and nitrous oxide emissions in the long run. In the short run, economic growth exerts a significant positive effect on methane emissions; however, its effect on CO2 and nitrous oxide emissions although positive was found to be statistically insignificant. Energy consumption produces an insignificant impact on all components of greenhouse gasses in the short run. In addition, our empirical results showed the presence of a non-linear relationship between methane emissions and economic growth, confirming the existence of the environmental Kuznets curve (EKC) only in the case of methane emissions model. [ABSTRACT FROM AUTHOR]

Several countries have weakened the carbon emission objectives to immediately revive the economy in the post-COVID-19 era. Therefore, it is a challenge worth addressing to readjust the economic development and carbon emissions after the COVID-19 pandemic. From the perspective of China's carbon emissions, this study shapes a multi-objective dynamic optimization model based on the material capital input and R&D support aspects. The proposed model imitates China's economic development, energy consumption, and carbon dioxide (CO2) emissions. The model provides theoretical suggestion for the government to revive economic development and reduce carbon emissions. In addition, this research paper compares the evolutionary path of carbon peak under the two scenarios. The first scenario requires maintaining the pre-epidemic development state and pace of carbon emission reduction, referred to as the baseline scenario (BS). The second scenario is termed the optimal scenario (OS) based on the model calculation. The study findings exhibit that China is not able to accomplish the 2030 CO2 emission peak objective, under the BS. However, China under the OS shall expectedly accomplish the 2030 carbon peak objective ahead of schedule, while the peak CO2 emissions shall be around 11.28 billion tons. Reportedly, at least 788 million tons of CO2 reduction contrasted with the BS. Furthermore, there is an 80.35% decline in energy intensity as compared to 2005. Consequently, the study results contribute theoretical guidance for the "green recovery" of China's economy and the adjustment of carbon emission reduction's path after the COVID-19 epidemic. Consistent with this, the research method also contributes to the theoretical research on carbon emissions at the national level while extending a new research perspective for the economic and environmental fields. [ABSTRACT FROM AUTHOR]

Climate change and global warming are the visible consequences of the increased amount of carbon dioxide (CO2) in the atmosphere. Among the various sources of anthropogenic CO2 emission, the diesel engine has a significant contribution. The development of a reliable system to efficiently minimize CO2 emissions from diesel engines to the safest level is lacking in the open literature. Therefore, a comprehensive multidisciplinary approach has been applied in this paper to investigate the efficacy of the post-combustion carbon capture (PCC) process for the diesel engine. The experiments have been performed on the exhaust of a direct injection diesel engine at five different brake powers with blends of aqueous ammonia (AQ_NH3), monoethanolamine (MEA), N,N-dimethylethanolamine (DMEA), and 1-ethyl-3-methylimidazolium tetrafluoroborate (C2mim BF4) ionic liquid (IL) as an absorbent for CO2 capture. The reaction mechanism of these absorbent with CO2 are also studied by the geometrical, energetical, MESP, frontier molecular orbitals, and NBO analysis using the first-principles density functional theory (DFT) calculations. The maximum CO2 absorption efficiency of almost 97% was achieved for the blend consisting of 67% of AQ_NH3 and 33% of MEA. Moreover, AQ_MEA and blend of AQ_NH3, DMEA, and C2mim BF4 ionic liquid showed 96% and 94% CO2 absorption efficiency, respectively. [ABSTRACT FROM AUTHOR]

Fossil fuel electricity generation in Saudi Arabia increased greatly from 1980 to 2017. This paper aims to quantify the electricity generation effect on the environmental quality of Saudi Arabia and explore the role of energy-efficient technological innovation. A structural time series model (STSM) to estimate long-run elasticities and logarithmic mean Divisia index (LMDI) is employed. The results showed that variables (GDP, electricity generation, and population) have a significant effect on carbon dioxide (CO2) emissions. Also, the underlying energy demand trend (UEDT) showed an upward slope for the entire period, which suggests that over the study time there is no improvement in energy efficiency. In decomposing the factors for carbon emissions growth in Saudi Arabia, the findings of applying additive LMDI analysis showed a 1377.56 million tonne (MT) increase in CO2 emissions from the three factors between 1980 and 2017 in the country. The results of additive decomposition showed that the primary factor that drives the carbon emissions growth in Saudi Arabia was the structure effect. Saudi Arabian policymakers could make more informed decisions regarding electricity generation by focusing on increasing energy efficiency and demanding strict environmental regulations to contribute to sustainable economic growth. [ABSTRACT FROM AUTHOR]

Unlike the previous study, this paper employs panel cointegration and Granger causation approaches to discuss the associations among carbon dioxide (CO2) emissions, GDP growth, clean energy generation, and industrial growth for the top ten industrial countries spanning the period 1980-2014. The primary empirical outcomes show a two-way long-run association between environmental indicator, GDP growth, and clean energy generation, while one short-run causation from clean energy generation to CO2 emissions and from industrial growth to clean energy generation. The computed coefficients elasticity's under FMOLS, DOLS, and CCR estimates revealed that the clean energy generation statistically contributes to declining emissions of CO2 in Australia, Austria, and Chile while statistically increase emissions of CO2 in Denmark and the Netherlands. Industrial growth statistically contributes to reducing emissions of CO2 in Denmark and Norway but increases emissions in Chile, France, and Sweden. For the global panel, industrial growth leads to mitigate the rate of emissions while clean energy generation raises CO2 emissions in the long period. Investing in clean energy is needed to stimulate the growth of the industrial sector and then reduce the rate of emissions. [ABSTRACT FROM AUTHOR]

The paper examines the relationship between poverty and CO2 emission. We contribute by using a global panel data of 146 countries over the period 1996-2014 and by focusing on the role of institution in poverty and environmental degradation nexus. The main findings are that the non-linearity relationship between poverty and CO2 emission could prompt a further increase in the poverty and environmental degradation. However, an increase in the institutional quality leads to a reduction in poverty and more protection to the environment. The main policy recommendation is that all countries need to improve their institutional framework so that reduction of poverty and environmental degradation can occur. [ABSTRACT FROM AUTHOR]

China has announced to launch a national emission trading system (ETS). The heterogeneity of marginal abatement cost (MAC) is prerequisite for trading, and the knowledge about the evolutionary characteristics of MAC is particularly necessary. However, the β convergence theory has been proved to be suitable yet rarely applied to the study of MAC of CO2. To fill this gap, this paper connects them creatively, and the convergence of MAC during 2001–2015 and the influential factors are explored by spatial panel data models. Results show that China's MAC converges during the study period whether the spatial effect is considered or not. When evaluating the convergence of MAC, the spatial effect should not be ignored, because it will improve the explanatory power of models and promote the convergence. The size of labor force, emission level, coal consumption, foreign direct investment, and industrial structure significantly affect the growth rate of MAC. Low-carbon policies could be formulated fully considering the factors and their spillover effects. Those findings are certainly significant in imposing carbon reduction targets and adopting policy instruments. In addition, a national ETS is more applicable to China's reality at this stage and suggested to introduce carbon tax in due course in the future. [ABSTRACT FROM AUTHOR]

This paper uses the pollution haven hypothesis (PHH) to examine the effects of foreign direct investment (FDI) and government expenditures on environmental quality in Korea. For this purpose, we conducted autoregressive distributed lag (ARDL) bounds testing for the period 1970–2018. Empirical results indicate that in Korea, increased FDI, per capita gross domestic product (GDP), energy use, and imports have led to increased per capita carbon dioxide (CO2) emissions, while government expenditures, renewable energy, and exports have led to decreased per capita CO2 emissions. Empirical results also reveal an N-shaped relationship between per capita GDP and per capita CO2 emissions. Moreover, the PHH is valid in Korea to a moderate degree, and the detrimental effect of FDI on environmental quality is relatively limited. Furthermore, government expenditures improve environmental quality. These empirical findings support Korea's leading role in implementing comprehensive green growth initiatives. [ABSTRACT FROM AUTHOR]

Foresight of CO2 emissions from fuel combustion is essential for policy-makers to identify ready targets for effective reduction plans and to further improve energy policies and plans. A new method for forecasting the future development of China's CO2 emissions from fuel combustion is proposed in this paper by using grey forecasting theory. Although the existing fractional nonlinear grey Bernoulli model (denoted as FNGBM(1,1)) has been theoretically proven to enhance the adaptability to diverse sequences, its fixed integer-order differential derivative still impairs the performance to some extent. To this end, a varying-order differential derivative is introduced into the existing differential equation to enable a more flexible structure, thus improving the prediction ability of FNGBM(1,1). Specifically, because of the advantages of conformable fractional accumulation, the traditional differential derivative is first replaced by the conformable fractional differential derivative. As a consequence, the continuous conformable fractional nonlinear grey Bernoulli model (hereinafter referred to as CCFNGBM(1,1)) is proposed. To further increase the validity of the model, a metaheuristic algorithm, namely Grey Wolf Optimizer (GWO), is then applied to search for the optimal emerging coefficients for the proposed model. Two real examples and China's CO2 emissions from fuel combustion are considered to verify the effectiveness of the newly proposed model, the experimental results show that the newly proposed model outperforms other benchmark models in terms of forecasting accuracy. The proposed model is finally employed to forecast the future China's CO2 emissions from fuel combustion by 2023, accounting for 10,039.80 million tons. Based on the forecasts, several policy suggestions are provided to curb CO2 emissions. [ABSTRACT FROM AUTHOR]

The energy transition from coal and oil to renewable energy, nuclear energy, and natural gas is a fundamental way for emission reduction of China's power generation sector. Until now, research on the drivers of CO2 emissions from China's power generation sector has generally evaluated the energy mix as a whole, with a lack of exploration of the decomposition of different types of energy. This paper uses both index decomposition analysis (IDA) and structural decomposition analysis (SDA) to explore the impacts of energy transition on CO2 emissions in the power generation sector during periods of 2002–2007, 2007–2012, and 2012–2017. We find that the results of IDA and SDA are almost consistent, indicating that our results are robust. During the whole study period, CO2 emissions of power generation sector increased by 2447 Mt, of which the fossil fuel structure significantly contributed 642 Mt of incremental emissions (IDA). The thermal power generation efficiency was a dominator for reducing emissions, with a total reduction of 586 Mt (IDA). Simultaneously, the impacts of renewable energy and nuclear energy on emission reduction tend to be strengthening over time, with values changing from 38 Mt and −5 Mt in 2002-2007 to −219 Mt and −83 Mt (IDA) in 2012-2017, respectively. Based on the results, we put forward some suggestions such as promoting coal-to-gas, renewable energy, and nuclear energy in power generation to cut down CO2 emissions of China's power generation sector. [ABSTRACT FROM AUTHOR]

Carbon dioxide emission is an important environmental issue, and it has also become an important reference factor for governments to formulate social and economic policies. The objective and accurate prediction of carbon dioxide emissions can provide reference and early warning for the implementation of the government's environmental strategy. The change of the original data of carbon dioxide emissions is S-type, but not saturated S-type. The grey Verhulst model is mainly used to describe the process with saturation state, which is suitable for modeling S-type data series. However, it is found that there are inherent errors and limitations in this model. In this paper, the grey action of the grey Verhulst model is improved, a new action Verhulst model is obtained, and its properties are studied. Finally, the new model is used to predict the carbon dioxide emissions of China and Russia, and ARIMA model is added for comparison. The results show that compared with the original Verhulst model, the simulation and prediction accuracy of the optimized Verhulst model are improved by more than 10%, and the ARIMA model underestimates the carbon dioxide emissions. From the result analysis, China and Russia need to formulate strong energy conservation and emission reduction policies, vigorously develop clean energy industry, and promote green production and lifestyle. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to find the effects of financial development, income inequality, energy usage, and per capita GDP on carbon dioxide (CO2) emissions as well the environmental Kuznets curve (EKC) for the three developing Asian countries—Bangladesh, India, and Pakistan. Panel data during the period 1980-2014 and the Stochastic Impacts by Regression on Population, Affluence, and Technology model with fully modified ordinary least squares (FMOLS) are employed for empirical investigation. The results show that financial development has a significant negative relationship with CO2 emission in the three selected Asian countries with the exception of India. The results further reveal that income inequality in Pakistan and India reduce CO2 emission, while the result for Bangladesh is opposite. Likewise, energy usage has a significant positive effect on CO2 emission in Bangladesh, Pakistan, and India. Our empirical analysis based on long-run and short-run elasticity appraisal suggests the validation of the EKC in Pakistan and India. The study findings recommend an important policy insinuation. The study suggests introducing a motivational campaign for the inhabitant towards utilization of high-efficiency electrical appliances, constructing mutual cooperation for economic development rather involve in winning development race, and introducing effective pollution absorption measures along with big projects. [ABSTRACT FROM AUTHOR]

One of humanity's most significant problems in the twenty-first century revolves around how to balance the mitigation of environmental pollution while achieving sustainable economic development. Despite increased awareness and dedication to climate change, the planet is still seeing a drastic decrease in the volume of pollutant emissions. This study explores the long-run and causal impact of economic growth, financial development, urbanization, and gross capital formation on Malaysia's CO2 emissions based on the STIRPAT framework. The current paper employs recently developed econometric techniques such as Maki co-integration, auto-regressive distribution lag (ARDL), fully modified OLS (FMOLS), dynamic ordinary least square (DOLS), and wavelet coherence and gradual shift causality tests to investigate these interconnections. The advantage of the gradual shift causality test is that it can capture the causality in the presence of a structural break(s). The findings from the Maki co-integration and ARDL bounds tests reveal evidence of cointegration among the variables. The ARDL test reveals that economic growth, gross capital formation, and urbanization exert a positive impact on CO2 emissions. Furthermore, the wavelet coherence test reveals that there is a significant dependency between CO2 emissions and economic growth, gross capital formation, and urbanization. The Toda Yamamoto and Gradual shift causality tests reveal that there is a (a) unidirectional causality from urbanization to CO2 emissions, (b) unidirectional causality from economic growth to CO2 emissions, and (c) unidirectional causality from gross capital formation to CO2 emissions. [ABSTRACT FROM AUTHOR]

The paper aims to investigate the influencing factors that drive the temporal and spatial differences of CO2 emissions for the transportation sector in China. For this purpose, this study adopts a Logistic Mean Division Index (LMDI) model to explore the driving forces of the changes for the transport sector's CO2 emissions from a temporal perspective during 2000–2017 and identifies the key factors of differences in the transport sector's CO2 emissions of China's 15 cities in four key years (i.e., 2000, 2005, 2010, and 2017) using a multi-regional spatial decomposition model (M-R). Based on the empirical results, it was found that the main forces for affecting CO2 emissions of the transport sector are not the same as those from temporal and spatial perspectives. Temporal decomposition results show that the income effect is the dominant factor inducing the increase of CO2 emissions in the transport sector, while the transportation intensity effect is the main factor for curbing the CO2 emissions. Spatial decomposition results demonstrate that income effect, energy intensity effect, transportation intensity effect, and transportation structure effect are important factors which result in enlarging the differences in city-level CO2 emissions. In addition, the less-developed cities and lower energy efficiency cities have greater potential to reduce CO2 emissions of the transport sector. Understanding the feature of CO2 emissions and the influencing factors of cities is critical for formulating city-level mitigation strategies of the transport sector in China. Overall, it is expected that the level of economic development is the main factor leading to the differences in CO2 emissions from a spatial-temporal perspective. [ABSTRACT FROM AUTHOR]

Mitigating environmental pollution has become a global agenda keeping the sustainability of socioeconomic and environmental development into cognizance. Against this backdrop, this paper aims to evaluate the authenticity of the environmental Kuznets curve hypothesis in the context of Bangladesh; particularly controlling for different types of energy consumption including coal, natural gas, liquefied petroleum gas, refined petroleum oil, and hydropower. The results from the robust econometric analysis, accounting for multiple structural break concerns in the data, authenticate the validity of the environmental Kuznets curve hypothesis. Besides, the predicted economic growth thresholds are found to be higher than the current level which indicates that Bangladesh is still at a development phase where the nation is trading-off environmental deterioration for economic expansion. Moreover, the results also show that consumption of coal and refined petroleum oils increase emissions of carbon dioxide and aggregate greenhouse gases while higher consumption of natural gas, liquefied petroleum gas, and hydropower are seen to abate these emissions. Thus, in line with these findings, it can be asserted that both natural gas and liquefied petroleum gas can function as transitionary fuels to mitigate environmental pollution in Bangladesh before the nation can completely undergo renewable energy transition. Hence, these findings impose key policy takeaways concerning energy diversification and environmental sustainability in Bangladesh. [ABSTRACT FROM AUTHOR]