CO2 Reduction Policies Research Articles

Navigating the nexus: unraveling technological innovation, economic growth, trade openness, ICT, and CO2 emissions through symmetric and asymmetric analysis

In Malaysia’s rapid economic growth and industrialization, environmental degradation and carbon emissions pose significant challenges. As urbanization continues to rise, there is a growing recognition of the imperative to tackle CO2 emissions. Trade openness and globalization drive economic activity but also heighten environmental pressures, including CO2 emissions from transportation and industry. Information communication technology (ICT) usage, shaped by infrastructure and regulations, can either improve energy efficiency or increase energy consumption. The study examines the impacts of economic growth (EG), trade openness (TON), technological innovation (TIN), and ICT on CO2 emissions in Malaysia, using both symmetric and asymmetric methods from 1985 to 2021. While many studies have explored environmental degradation, focusing on CO2 emissions and ecological footprint indicators, only a limited number have delved into the combined impact of sustainable EG, TON, ICT, and TIN on Malaysia’s CO2 emissions. Notably, these studies have often neglected the utilization of both symmetric and asymmetric methodologies. Hence, this study employed auto-regressive distributed lag (ARDL) and non-linear ARDL approaches to investigate the dynamic effects of the studied variables. The key findings from the symmetric analysis demonstrate that EG, TON, and ICT together take part in the increase of CO2 emissions in both the short and long run. Particularly, technological innovation plays a significant role in reducing CO2 emissions in the short term through the adoption of cleaner technologies. However, the results of the NARDL bound test reveal asymmetric long-term consequences of technological innovation, economic growth, and ICT on CO2 emissions. The study underscores the need for CO2 reduction policies in Malaysia, advocating for measures, such as incentivizing cleaner technologies and upgrading energy infrastructure. It also recommends implementing carbon pricing mechanisms for production and trade, alongside awareness campaigns to foster behavioral changes aimed at reducing emissions.

Motivated reasoning about climate change and the influence of Numeracy, Need for Cognition, and the Dark Factor of Personality

Human information processing is not always rational but influenced by prior attitudes, a phenomenon commonly known as motivated reasoning. We conducted two studies (N1 = 556, N2 = 1198; UK samples) investigating motivated reasoning in the context of climate change with a focus on individual differences as potential moderating factors. While previous research investigated motivated reasoning regarding the debate whether climate change is anthropogenic, we focused on current discourses about the effectiveness of different countermeasures. To this end, participants evaluated fictitious scientific data on the effectiveness of regulations to reduce CO2 emissions. In both studies, participants exhibited motivated reasoning as indicated by the observation that prior attitudes about CO2 reduction policies predicted evaluation of the scientific data. The degree of motivated reasoning was not related to individual difference variables, namely the ability to understand and reason with numbers (Numeracy), the willingness to show this ability (Need for Cognition), and the tendency to maximize one’s individual utility (Dark Factor of Personality). However, numeracy was associated with a less biased interpretation of the presented information. Our research demonstrates that motivated reasoning is a general phenomenon, and points to numerical training as one way to improve reasoning.

Co-abatement of carbon and air pollutants emissions in China’s iron and steel industry under carbon neutrality scenarios

Co-abatement strategies that can reduce both CO2 and air pollutants emissions are essential for the green and low-carbon transition of China’s iron and steel industry. Most previous studies analyzed CO2 and air pollutants reduction policies independently, but the synergies and tradeoffs between the two types of policies have rarely been investigated. By establishing a bottom-up simulation model that considers combinations of different policy objectives, crude steel demand and technology development options, this study couples air pollutants reduction into the carbon neutrality pathways to evaluate the synergistic effects of different abatement strategies in China’s iron and steel industry. The study also incorporates the indirect CO2 and air pollutants emissions from the upstream energy production sectors into pathway analysis, in order to avoid cross-sectoral carbon and air pollution leakages. The results show that the current emissions reduction policies in the iron and steel industry are far from achieving the carbon neutrality goals. To achieve synergistic reduction in CO2 and air pollutants emissions, the short term policies should mainly rely on increasing the share of the scrap-electric arc furnace (EAF), while reducing crude steel demand and promoting breakthrough technologies, particularly hydrogen-based direct reduction, should play a major role in the long term. Meanwhile, China should promote the decarbonization of power generation and hydrogen production to reduce indirect CO2 and air pollutants emissions from the upstream sectors.

Impacts of Land Urbanization on CO2 Emissions: Policy Implications Based on Developmental Stages

The systematic advancement of land urbanization coupled with the pursuit of a low-carbon economy constitutes a critical challenge faced by numerous nations across the globe. Utilizing panel data spanning 195 countries from 1990 to 2020, this study employed a panel regression model to scrutinize the impact mechanisms of land urbanization on CO2 emissions across income groups. The findings revealed a consistent overall increase in both land urbanization and per capita CO2 emissions during the period examined, with marked disparities evident among countries of varying income levels. The regression analysis further identified an “inverted U-shaped” curve relationship between land urbanization and per capita CO2 emissions in the global context and within high-income panels. Conversely, a “U-shaped” curve relationship was discerned in lower-middle-income nations, whereas a linear relationship was observed in upper-middle-income and low-income countries. These insights serve to deepen the understanding of the CO2 emission implications of land urbanization across countries at different development stages. This study underscores the necessity for countries to attentively consider their unique stage of development when devising CO2 reduction policies, reinforcing the complex interplay between urbanization, economic categorization, and environmental stewardship.

Driving factors of energy related CO2 emissions at a regional level in the residential sector of Iran

Iran has increased its CO2 emissions significantly during the past few decades. The household sector in Iran contributes one of the largest sectors of CO2 emissions. Despite this significant contribution, the existing policies have predominantly concentrated on large-scale initiatives while overlooking the regional role in shaping and implementing these plans. Therefore, this study investigates the relationship between CO2 emissions and the efficient factors in three major groups including energy, climate, and household socio-economic factors. This study aims to address regional carbon emissions and develop CO2 reduction policies tailored to each region's specific circumstances. It focuses on planning strategies at the regional level to effectively tackle CO2 emissions. Household panel data of 28 provinces of Iran are employed by using both static and dynamic panel models for the years 2001 to 2019. Static estimation includes Fixed Effect (FE), Random Effect (RE) and pooled Partial least squares (PLS), Dynamic estimation includes difference Generalized Method of Moments (GMM) and system Generalized Method of Moments (GMM). The empirical result of the static method showed positive dependence of household CO2 emissions on Heating Degree Days (HDD), Cooling Degree Days (CDD), precipitation level, oil consumption, gas consumption, household income, size of household, and also building stocks. In more detail, educational rate, dummy variable (removal of energy subsidy), and oil price reveal the greatest negative impact on the emissions with elasticities of − 0.428, − 0.31, and − 0.15; It represents 1% increase causes − 0.428, − 0.31, − 0.15, decrease CO2 emissions, respectively. however, household size, gas consumption, and oil consumption show the most significant positive effects on CO2 emissions with 1 percent increase causes CO2 emissions increases by 0.1, 0.044, and 0.026, respectively. Regarding the impact of climate factors, a 1% increase in Heating Degree Days, Cooling Degree Days, and precipitation level causes CO2 emissions increase by 0.024%, 0.004%, and 0.011% respectively, due to an increase in fossil energy demand. Results of the dynamic method of the system Generalized Method of Moments are similar to the static estimation results, except for that household size and urbanization are not significant. Also, removing the energy subsidy for fossil fuels due to substantial subsidy in fossil fuels in Iran or implementing a re-pricing energy policy can be a beneficial way to control carbon emissions from households within the provinces of the country. However, it is important to consider that this shift could potentially transfer subsidies to investments in the private sector for renewable energies.

Air quality related equity implications of U.S. decarbonization policy

Climate policies that target greenhouse gas emissions can improve air quality by reducing co-emitted air pollutant emissions. However, the extent to which climate policy could contribute to the targets of reducing existing pollution disparities across different populations remains largely unknown. We quantify potential air pollution exposure reductions under U.S. federal carbon policy, considering implications of resulting health benefits for exposure disparities across U.S. racial/ethnic groups. We focus on policy cases that achieve reductions of 40-60% in 2030 economy-wide carbon dioxide (CO2) emissions, when compared with 2005 emissions. The 50% CO2 reduction policy case reduces average fine particulate matter (PM2.5) exposure across racial/ethnic groups, with greatest benefit for non-Hispanic Black (−0.44 μg/m3) and white populations (−0.37 μg/m3). The average exposure disparity for racial/ethnic minorities rises from 12.4% to 13.1%. Applying an optimization approach to multiple emissions reduction scenarios, we find that no alternate combination of reductions from different CO2 sources would substantially mitigate exposure disparities. Results suggest that CO2-based strategies for this range of reductions are insufficient for fully mitigating PM2.5 exposure disparities between white and racial/ethnic minority populations; addressing disparities may require larger-scale structural changes.

Research on the Sustainable Development Path of Regional Economy Based on CO2 Reduction Policy

With the rapid growth of China’s economic growth, a large number of greenhouse gas emissions have led to a significant increase in environmental pressure. Currently, China has not yet achieved a good balance between greenhouse gas emissions and economic growth. To improve the sustainable development of China’s regional economy and effectively control domestic CO2 emissions, research is conducted to analyze the trend of regional economic change based on carbon emission policies. This study looks for suitable paths to achieve sustainable development of the regional economy. In this study, CO2 emissions were incorporated into an economic model to calculate the Green Total Factor Productivity (GTFP) efficiency value and its growth rate in each region of China. This was done to examine the productivity of each region in China. and it also aims to discuss the driving factors behind it, so as to give relevant policy suggestions that can help China’s sustainable economic development. The ultimate goal is to achieve sustainable RE development. The method used to measure the GTFP efficiency was the slacks-based measure (SBM) based on the data envelopment analysis (DEA) technique. The regression analysis of the relevant drivers was based on the regression analysis of the panel data model. The research results show that the level of urbanization and industrial structure were the main influencing factors for the increase of CO2 emissions. Consequently, macro-regulation can appropriately reduce CO2 emissions. In addition, the implementation of carbon emission reduction policies such as industrial structure optimization, education investment, and market-oriented reform also promote the sustainable development of the regional economy. Therefore, appropriate carbon emission reduction policies can improve the level of sustainable development of the regional economy. It also can ensure the stability of the regional environmental level.

Decomposition analysis of CO2 emissions of electricity and carbon-reduction policy implication: a study of a province in China based on the logarithmic mean Divisia index method

Abstract As the proportion of electricity in final energy consumption gradually increases, CO2 emissions reduction actions in the power system will become the key to achieving China’s carbon-peak and carbon-neutrality goals. It is essential to analyse and quantify the driving forces of CO2 emissions from electricity generation in the fossil-rich area in China. This paper aims to identify the characteristics of CO2 emissions generated by electricity and provide a basis for formulating CO2-reduction policies in power systems. First, we analyse the current state of CO2 emissions from electricity generation in Anhui Province that was dominated by fossil energy during the period 2010–19. Then, we apply the logarithmic mean Divisia index method to find the nature of the factors influencing the changes in CO2 emissions. Finally, we analyse the CO2-reduction measures of each side of the source–network–load–storage of the power system in Anhui through a power-system carbon-reduction path analysis model proposed in this study and provide policy suggestions. The results showed the following. (i) CO2 emissions in Anhui Province continued to increase from 2010 to 2019 and the trend in the growth rate of CO2 emissions presented approximately a u-shaped curve. (ii) Economic activity has always been the dominant factor driving the growth of electricity CO2 emissions. The increase in the proportion of renewable energy in power generation, the improvement in thermal power-conversion efficiency and the decrease in the intensity of power consumption are the three major driving factors for the reduction in CO2 emissions from power generation in Anhui. (iii) The CO2-reduction measures of the power system are provided in each link of the source–network–load–storage, such as developing the photovoltaic industry and building energy storage, upgrading and transforming coal-fired power stations, reducing the loss rate of transmission lines on the grid side and improving the efficiency of the utilization of electricity on the user side.

Experimental-based energy performance evaluation of low-cost retrofit strategy for aging low-rise residential building for carbon neutrality

The CO2 reduction policy has driven the remodeling policy to reduce the energy consumption of buildings, which can be implemented by insulating and replacing windows to reduce the energy consumption of buildings. However, due to energy poverty, many residents have limited opportunities for these projects. Therefore, economically accessible technologies were evaluated in this study. Specifically, practical technologies were applied and evaluated in practice, and a survey of 107 households was conducted to analyze the behavior of energy consumption behavior in the residential areas with buildings. The airtight performance of the window was improved by approximately 15% without replacing it, and energy consumption was reduced by 25%. In the case of electric energy consumption, the use of high-efficiency lighting showed the most direct reduction. However, the introduction of renewable energy into buildings that are not insulated can contribute to relative climate-dependent reductions, but whether direct reductions are possible needs to be examined more closely. Relatively inexpensive technologies have improved insulation performance and have a shorter payback period compared to window replacement, further solidifying economic feasibility.

Development of a Control-Oriented Ignition Delay Model for GCI Combustion

Increasingly stringent pollutant emission limits and CO2 reduction policies are forcing the automotive industry toward cleaner and decarbonized mobility. The goal is to achieve carbon neutrality within 2050 and limit global warming to 2 °C (possibly 1.5 °C) with respect to pre-industrial levels as stated in both the European Green Deal and the Paris Agreement and further reiterated at the COP26. With the aim of simultaneously reducing both pollutants and CO2 emissions, a large amount of research is currently carried out on low-temperature highly efficient combustions (LTC). Among these advanced combustions, one of the most promising is Gasoline Compression Ignition (GCI), based on the spontaneous ignition of a gasoline-like fuel. Nevertheless, despite GCI proving to be effective in reducing both pollutants and CO2 emissions, GCI combustion controllability represents the main challenge that hinders the diffusion of this methodology for transportation. Several works in the literature demonstrated that to properly control GCI combustion, a multiple injections strategy is needed. The rise of pressure and temperature generated by the spontaneous ignition of small amounts of early-injected fuel reduces the ignition delay of the following main injection, responsible for the torque production of the engine. Since the combustion of the pre-injections is chemically driven, the ignition delay might be strongly affected by a slight variation in the engine control parameters and, consequently, lead to misfire or knocking. The goal of this work was to develop a control-oriented ignition delay model suitable to improve the GCI combustion stability through the proper management of the pilot injections. After a thorough analysis of the quantities affecting the ignition delay, this quantity was modeled as a function of both a thermodynamic and a chemical–physical index. The comparison between the measured and modeled ignition delay shows an accuracy compatible with the requirements for control purposes (the average root mean squared error between the measured and estimated start of combustion is close to 1.3 deg), over a wide range of operating conditions. As a result, the presented approach proved to be appropriate for the development of a model-based feed-forward contribution for a closed-loop combustion control strategy.

Dynamic model of CO2 reduction policies: Another step towards a cleaner generation expansion

Cap-and-trade mechanisms and emission taxes are becoming increasingly widespread CO2 reduction policies in liberalized electricity markets. To set the design parameters of a CO2 reduction mechanism, analytical optimization frameworks are a commonly used tools by policy makers. However, such frameworks can suffer from issues related to emerging dynamic behaviors in electricity markets. This paper focuses on the relation between the parameter settings of CO2 reduction policies and dynamics of the electricity market. The authors present an analytical framework based on the system dynamics modeling that improves the understanding of the relation between the quantified impacts of CO2 reduction policies and parameter settings. The framework is based on the insight that the quantified impacts of CO2 reduction policies are attributed to the policy design, delays and mutual interactions inherent in the electricity market. The framework addresses both the quantified impacts and the rationale behind dynamic behaviors in the electricity market after implementing CO2 reduction policies. The framework is based on a causal loop diagram, illustrating the causal relation between different components within a system of electricity market. It offers policy makers a tool to assess the interactions between policy parameters and the structure of the electricity market. The framework is simulated under a power system using different scenarios of CO2 reduction policies and illustrates possible quantification of cleaner production, outperforming the base case scenario up to 63% CO2 reduction.

Fossil Fuel Consumption, CO<sub>2</sub> Emissions and Growth in High-Income Countries and Low-Income Countries

This paper investigates the pairwise causality and co-integration that links fossil fuel consumption (FFC), carbon dioxide (CO2) emissions, and real gross domestic product (RGDP) between low-income countries (LIC) and high-income countries (HIC). This comparative analysis is anchored on Lv et al. (2019). Lv et al. (2019) enable the analytical framework model utilized to investigate the causality between FFC and CO2, CO2 and RGDP, and FFC and RGDP in HIC and LIC. Data were obtained from world development indicator between 1960 and 2019. The results obtained are, as follows: There exists a unidirectional causality, thus the RGDP granger causes CO2 in HIC, and no causality between RGDP and CO2 in LIC. Also, the study found no causality between FFC and RGDP, and FFC and CO2 in HIC and LIC. The mixed inter-regional causality result showed that there exists bi-directional causality between RGDP and CO2 for HIC and LIC. This implies that RGDP in LIC granger causes CO2 in HIC, and CO2 in HIC granger causes RGDP in LIC. Hence, the presence of a regional super-wicked problem. Also, CO2 in HIC granger causes FFC in LIC. The result suggests that countries should seamlessly adopt proportionate mitigation and adaptation policies to reduce the pollution transmission between economies. The non-existence of pairwise co-integration between FFC, CO2, and RGDP in HIC and LIC connotes that the CO2 reduction policy should be a short-term public policy strategy with conscious and deliberate targeting to avoid long-run growth reversal. Therefore, this paper concludes that reducing FFC may not necessarily lead to a decline in growth vice versa. Thus, to achieve a low carbon economy and a high growth regime, the global community should adopt a techno-economic paradigm model that would accelerate growth within a low-carbon economy regime to realize the 45% carbon reduction target by 2030 and the 2050 net-zero emission target.

Spatiotemporal variations in urban CO2 flux with land-use types in Seoul

BackgroundCities are a major source of atmospheric CO2; however, understanding the surface CO2 exchange processes that determine the net CO2 flux emitted from each city is challenging owing to the high heterogeneity of urban land use. Therefore, this study investigates the spatiotemporal variations of urban CO2 flux over the Seoul Capital Area, South Korea from 2017 to 2018, using CO2 flux measurements at nine sites with different urban land-use types (baseline, residential, old town residential, commercial, and vegetation areas).ResultsAnnual CO2 flux significantly varied from 1.09 kg C m− 2 year− 1 at the baseline site to 16.28 kg C m− 2 year− 1 at the old town residential site in the Seoul Capital Area. Monthly CO2 flux variations were closely correlated with the vegetation activity (r = − 0.61) at all sites; however, its correlation with building energy usage differed for each land-use type (r = 0.72 at residential sites and r = 0.34 at commercial sites). Diurnal CO2 flux variations were mostly correlated with traffic volume at all sites (r = 0.8); however, its correlation with the floating population was the opposite at residential (r = − 0.44) and commercial (r = 0.80) sites. Additionally, the hourly CO2 flux was highly related to temperature. At the vegetation site, as the temperature exceeded 24 ℃, the sensitivity of CO2 absorption to temperature increased 7.44-fold than that at the previous temperature. Conversely, the CO2 flux of non-vegetation sites increased when the temperature was less than or exceeded the 18 ℃ baseline, being three-times more sensitive to cold temperatures than hot ones. On average, non-vegetation urban sites emitted 0.45 g C m− 2 h− 1 of CO2 throughout the year, regardless of the temperature.ConclusionsOur results demonstrated that most urban areas acted as CO2 emission sources in all time zones; however, the CO2 flux characteristics varied extensively based on urban land-use types, even within cities. Therefore, multiple observations from various land-use types are essential for identifying the comprehensive CO2 cycle of each city to develop effective urban CO2 reduction policies.

Analysis of Influencing Factors and Trend Forecast of CO2 Emission in Chengdu-Chongqing Urban Agglomeration

Urban agglomeration is a primary source of global energy consumption and CO2 emissions. It is employed as a major means of modern economic and social activities. Analysis of the temporal and spatial characteristics of CO2 emissions in urban agglomerations and prediction of the future trends of CO2 emissions in urban agglomerations will help in the implementation of CO2 reduction policies within region-wide areas. So, based on that, this study contains four aspects. Firstly, it calculates the energy CO2 emissions of China’s Chengdu-Chongqing urban agglomeration. Secondly, it analyzes the time and space changes in the area by using ArcGIS. Then, the STIRPAT model is used to investigate the factors influencing CO2 emissions, and the elasticity coefficient of the influencing factors is estimated using the ridge regression method, and the important influencing factors are screened on the basis of the estimated results, which are then used as input features for prediction. Finally, a combined prediction model based on the improved GM (1, N) and SVR models is constructed, and then the optimal solution is found through the particle swarm optimization algorithm. It sets up different CO2 emission scenarios to predict the energy CO2 emission of the region and its cities. The results show that, first, the CO2 emissions of the Chengdu-Chongqing urban agglomeration have accumulated year by year, but by 2030, as predicted, it will not reach its peak. The spatial layout of CO2 emissions in this region is not expected to undergo major changes by 2030. Second, population, GDP, gas and electricity consumption, and industrial structure have served as important factors affecting energy CO2 emissions in the region. Third, on the basis of the prediction results for different scenarios, the CO2 emissions in the baseline scenario are low in the short term, but the CO2 emissions in the low-carbon scenario are low in the long run. This study also puts forward some policy recommendations on how to reduce CO2 emissions.

Sector linkages and driving forces of Chinese household CO2 emissions based on semi-closed input-output model.

For decades, the rapid urbanization and economic development in China inevitably lead to the increase of household energy demand and CO2 emissions. Existing studies regard the household sector as an exogenous sector unrelated to production, ignoring that the wages, goods, and services provided by production sectors will affect the consumption of the household sector. This paper analyzes the impact of the household sector on production sectors and the total CO2 emissions by establishing a semi-closed environmentally extended input-output model in China from 2007 to 2017. Through the calculation of index of the power of dispersion and index of sensitivity of dispersion, the index of sensitivity of dispersion of household is large, and household has push to several sectors' CO2 emissions. Furthermore, we explore the drivers of China's household CO2 emissions using structural decomposition analysis and claim that consumption per capita and urbanization rate are the dominant factors driving household CO2 emissions, while carbon intensity and economic structure are the main factors inhibiting household CO2 emissions. Our research shows a clear picture of sector linkages and driving forces of household CO2 emissions, providing strong evidence for the development of China's household CO2 reduction policies.

Investigating the Dynamic Impact of FDI Inflows and Economic Growth on Environmental Degradation: Evidence From FMOLS and DOLS for Selected Asian Countries

The study examines the dynamic relationship between foreign direct investment (FDI) inflows, economic growth, and environmental degradation and investigates the long-run validity of the environmental Kuznets curve (EKC) and the pollution haven hypothesis (PHH) for selected Asian countries over the period 1990–2019. Additionally, this study aims to discover the longrun impact of energy consumption, globalization, and population density on environmental degradation by employing a panel cointegration approach, fully modified ordinary least squares (FMOLS), and dynamic ordinary least squares (DOLS). The findings provide clear evidence of the existence of EKC and PHH in Asian countries for the period 1990–2019 in the long run. The findings reveal that economic growth has a highly significant and positive role in depleting environmental quality, but this effect gets reversed in the long run as, after a certain turning point, economic growth increases, and the quality of the environment gets better. Moreover, FDI inflows and energy consumption have a positive long-run impact on CO2 emissions, thus contributing to environmental degradation. The study recommends that governments and policymakers should strategically devise and implement CO2 reduction policies, such as carbon pricing, to encourage economic growth and to improve the quality of the environment, with the ultimate goal being to achieve sustainable development. Moreover, the use of cleaner energy should be promoted, and innovations and technological developments should be encouraged for hydropower, wind power, solar energy and other facilities around the world.

How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method

Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions.

Decoupling Analysis of CO2 Emissions in the Industrial Sector from Economic Growth in China

China has become the largest CO2 emission country since 2014. The industrial sector is the largest contributor to CO2 emissions in China. This paper uncovers the spatiotemporal characteristics of the decoupling status of industrial CO2 emissions from economic growth at the provincial level during 1995–2019 in China and analyzed the structural characteristics of the industrial CO2 emissions. The results suggested that 2010 is an important turning point. Since 2010, the decoupling status of industrial CO2 emissions from economic growth has kept a continuously improving trend. During 2016–2019, all provinces achieved decoupling of the industrial CO2 emissions from economic growth. More than 20% achieved absolute strong decoupling. Four subindustries, including raw chemical materials and chemical products, production and supply of electric power and heat power, petroleum processing and coking products, and smelting and pressing of non-ferrous metals, with large CO2 emissions’ contribution and a continuously increasing trend, should be paid more attention in the future CO2 reduction policies formulation.

Energy policy trade-offs in Poland: A best-worst scaling discrete choice experiment

This study applied a discrete choice experiment using best-worst scaling questions (multi-profile, BWS case 3) to estimate the trade-offs which a sample of the Polish population is willing to make for energy reform regarding carbon reduction. Attributes considered in the study are CO2 emissions reduction, National energy independence, Employment impact, Time needed for policy transition, and Impact on household energy prices. Respondents (n = 639) choose the best and worst of the presented policy options to fit a rank-ordered logit model. This study reveals concern about climate change among respondents, but that they prioritize energy prices and employment in their choice of preferred energy policy with significant variation across groups. A key distinction is revealed in the preferences by age cohort in which youngest (<25years old) respondents had the strongest WTP coefficients, particularly for CO2 and time to transition. Stronger and more urgent desires for CO2 reduction policies were also identified among the decisions of female respondents, those having more education, those without children, and middle and upper income groups. The respondents’ choices also revealed consistent loss aversion in all attributes presented in the choice experiment.

Research on Inter-Provincial Transfer of CO2 Emissions from Transportation by Considering Fuzzy Parameter

CO2 reduction from transportation is exerting significant effects on global CO2 reduction. This industry contributes 23.96% of global CO2 emissions. In this research, an ecological network input–output interval fuzzy linear programming (EIFP) method is developed to clarify CO2 reduction responsibilities and depict transfer relationships of transportation. This method integrates input–output analysis (IOA), inexact rough interval fuzzy linear programming (IRFLP) and ecological network analysis (ENA) into a general framework. The proposed method is employed for calculating inter-provincial CO2 transfer under different situations in 30 provinces of China and further supporting the formulation of regional reduction policies. Results demonstrate that transportation energy demand of Beijing is dependent on imports, which indirectly increases CO2 reduction pressure in energy supply areas. Therefore, CO2 reduction responsibility should be traced to source and included in emission reduction plan of energy demand areas. In inter-provincial CO2 transfer relationships of natural gas, positive relationships account for a higher proportion; therefore, it is feasible to consider raising the proportion of natural gas in the future development direction of transportation. The achievements of this paper can provide scientific references for decision makers to formulate CO2 reduction policies in transportation.