Kaya Identity Research Articles

Analysis of influencing factors of industrial green and low-carbon transformation under the background of “double carbon”: evidence from Sichuan province, China

Introduction: Industrial green and low-carbon transformation is the key to improve economic development and necessary process to achieve the goal of the carbon peaking and carbon neutrality. Few studies have been done on the decomposition of carbon emission factors in industries and sub-industries and the impact of green and low-carbon transformation about carbon emission in each industry quantitatively. However, the study of industries and sub-industries can comprehensively analyze the development path of green and low-carbon transformation from a more detailed perspective, and provide scientific reasons for the optimization of industrial structure and energy structure.Methods: The extended Kaya identity for industrial carbon emission is constructed to obtain four factors influencing industrial carbon emission: economic output effect, industrial structure effect, energy intensity effect, carbon consumption intensity in this paper. Then, the LMDI decomposition method is combined with the above identity to innovatively obtain the contribution value of carbon emissions from the perspective of overall, industrial sector and tertiary industry. Then, based on the results of factor decomposition, a multi-index scenario prediction model is constructed. On this basis, the extreme learning machine model optimized by particle swarm optimization (PSO-ELM) was used to predict the influence of the changes in the driving factors on the reduction of industrial carbon emissions. By setting the baseline and industrial green and low-carbon transformation scenarios, it is predicted that industrial carbon emission in Sichuan Province.Results and discussion: (1) Economic output effect always promotes the growth of industrial carbon emissions, and with the adjustment of industrial structure and energy structure, the other three factors begin to restrain the growth of carbon emissions. (2) Scenario prediction shows that without considering the economic costs of transformation, improving carbon emission reduction efficiency can be obtained through accelerating the rate of change of industrial structure of the secondary and tertiary industries, increasing the proportion of energy intensity reduction, and strengthening the proportion of non-fossil energy use.

Decoupling for a greener future: a spatio-temporal analysis of CO2 emissions and economic growth.

Climate change mitigation is a pressing global challenge that requires reducing CO2 emissions without hindering economic growth. Using an extended Kaya identity, Logarithmic Mean Divisia Index (LMDI), and Tapio decoupling indicator, this paper investigates the spatio-temporal variations, drivers, and decoupling of CO2 emissions from economic growth in 150 countries from 1990 to 2019, considering regional disparities and income-based inequalities. The findings reveal increasing CO2 emissions between 1990 and 2019, with notable fluctuations in certain 5-year intervals. CO2 emission growth varied significantly by region, with countries like China, the USA, India, and Japan experiencing rapid increases. Economic growth emerged as the primary driver of CO2 emission growth, and its impact strengthened over time. Population growth also contributed significantly to CO2 emissions, particularly in middle- and low-income countries. The study identifies energy and carbon intensity as crucial mitigating factors that weaken CO2 emissions, offering hope for effective climate change mitigation. Furthermore, the degree of decoupling between economic growth and CO2 emissions varied among countries in the same region, with high-income countries demonstrating stronger decoupling compared to upper-middle-income countries, which accounted for 71% of global CO2 emission increase. These findings underline the imperative of accounting for income levels and regional differences in formulating CO2 emission mitigation strategies. Also, the study emphasizes the pressing necessity for cohesive global coordination to facilitate the transition toward a low-carbon economy. Such collaborative endeavors are paramount in our collective pursuit to combat climate change effectively, safeguarding the well-being and sustenance of our planet for future generations. As policymakers, it is imperative to integrate these insights into decision-making processes to chart a sustainable and resilient course forward.

Prediction of CO2 emission peak and reduction potential of Beijing-Tianjin-Hebei urban agglomeration

Forecasting the future emission trajectories and the relating sensitive driving factors of emissions for cities is of great significance to formulate realizable CO2 mitigation policies. To proceed the forecasting, studies on peak prediction and quantification of reduction potential at the city level are essential. However, the studies in the area are very limited. Selecting the Beijing-Tianjin-Hebei urban agglomeration (BTH) as the study region, this paper aims to contribute to the research area and provides implications for other cities or urban agglomerations. The Kaya identity and multi-scenario simulation were employed to predict the dynamic evolution pathways of CO2 emissions from 2021 to 2035 and explore the differential CO2 peak time, peak value, and reduction potential for 13 cities in BTH. Monte Carlo simulation, Mann-Kendall trend test and Sen's slope estimation method are jointly used to reduce uncertainties in estimation. The Monte Carlo simulation results show that most cities in BTH have already reached their CO2 emissions peak, while Tianjin, Langfang, Cangzhou and Tangshan are expected to reach their peaks between 2025 and 2030. Among them, 5 and 8 cities have the risk of not reaching their peak before 2035 in the high consumption scenario (HCS) and extensive development scenario (EDS) respectively. Comparative analysis reveals that low-carbon scenario (LCS) and sustainable development scenario (SDS) have significant effects on emissions reductions. The top three cities in terms of accumulative emission reduction in 2021–2035 are Tianjin, Tangshan and Cangzhou, estimated as 117.82–250.75 Mt CO2 in LCS and 217.77–454.10 Mt CO2 in SDS, respectively. The results of sensitivity analysis reveal that the most critical driver of CO2 emissions in Beijing is population, while that is GDP per capita for Tianjin. Langfang and Hengshui showed the highest sensitivity to energy intensity. Accordingly, these cities have differentiated concerns and priorities to achieve their carbon peak goal as scheduled.

The Impact of Economic Growth and Urbanisation on Environmental Degradation in the Baltic States: An Extended Kaya Identity

The main aim of this article is to empirically examine the impact of economic growth and urbanisation on environmental degradation, as well as the existence of the environmental Kuznets curve (EKC) in three Baltic States (Lithuania, Latvia, and Estonia) from 2000 to 2020. The main Kaya identity and the extended urban Kaya identity models are applied within the analysis. The multiple regression analysis made it possible to assess the influence of urbanisation and other factors on greenhouse gas (GHG) emissions in the studied countries, as well as test the hypothesis of the inverted U-shaped EKC. The main finding reveals that GDP per capita growth has the largest and increasing effect on GHG emissions in all three countries. It was also found that changes in population in urban areas in Lithuania and Latvia reduced the amount of GHG until 2020, while in Estonia, the growing urban population greatly contributed to increasing GHG emissions. As a result, processes related to urbanisation have not yet had a significant impact on environmental quality in Lithuania and Latvia. Meanwhile, in Estonia, this is a significant factor that policymakers need to focus on when solving environmental pollution reduction problems. The hypothesis of the EKC was mostly supported when analysing GHG emissions in Lithuania and Estonia and using GDP per capita as an indicator for economic growth. On the other hand, it was found that the impact of the urbanisation rate on GHG emissions is not curved, yet there is some evidence that in Estonia, a growing urbanisation rate is related to diminishing GHG emissions, according to the multiple regression analysis. The results of the study showed that policymakers should consider economic growth and, especially in Estonia, urbanisation when solving problems related to environmental degradation.

Empirical linkages of the construction sector, intensive energy consumption, and economic openness with chemical oxygen demand pollution.

Though construction sector development and economic openness contribute to regional economic development, they have also been debated to pose some environmental challenges. Along these lines, we explored the long- and short-term connections of intensive energy consumption, economic openness, and construction sector development with the chemical oxygen demand throughout the scales of regional development of China's 30 provincial units over the 2004-2021 period. Theoretically, we contribute to the existing knowledge by incorporating chemical oxygen demand pollution, construction sector development, and economic openness to the Kaya identity's baseline framework. Empirically, we apply a series of advanced methods of panel data econometrics for robust results. Our key findings are as follows: First, we revealed a long-term stable cointegrating association among our variables of interest. Second, using the common correlated effect mean group estimator, we unfolded that the intensive energy consumption showed a chemical oxygen demand pollution reduction influence in both the long and short term, demonstrating the most substantial influence in the high regional development panel while expressing the least powerful influence the least regional development setting. Third, we unveiled that economic openness and construction sector development showed a linear chemical oxygen demand pollution enhancement influence in moderately and least developed regions. Nevertheless, both established an inverted U-shaped linkage with chemical oxygen demand pollution for the whole country as well as for high regional development data samples. Eventually, we found consistent estimates across long- and short-term investigations regarding signs of relationships; however, long-term effects remained more powerful than short-term ones. These findings would serve as factual scientific knowledge to help local as well as national governments create the optimal environmental regulations for the construction sector to achieve the sustainable development goals (SDGs), especially the Climate Action Plan (i.e., SDG-13).

Investigating CO2 emissions and disparity from China's central heating: A perspective at the city level

The decarbonization of China's central heating (CH) is important for reaching China's carbon peak and carbon neutrality goal. However, unclear CO2 emissions from CH of each city and their disparity hinder the formulation of targeted decarbonization strategies. To solve these issues, this study quantifies the CO2 emissions from CH in 137 cities during the period of 2006–2019 and introduces the Kaya identity and Theil index to investigate the disparity in the per capita CO2 emissions from CH. The results indicate that 1) China's CH CO2 emissions increased to 435.53 MtCO2 in 2019. Residential and public and commercial (P&C) buildings accounted for 74.18% and 25.82%, respectively. 2) The cities with the largest CH CO2 emissions of all (22.58 MtCO2), residential (16.33 MtCO2), and P&C buildings (6.86 MtCO2) were Harbin, Shenyang and Harbin, respectively. 3) The Theil index for all buildings decreased to 0.44 in 2019, and P&C buildings had the largest disparity. 4) The central heating penetration rate was the major positive contributor to the disparity of residential and P&C buildings, contributing more than 40%. In summary, at the city level, this study offers a comprehensive investigation of the CO2 emissions from CH and benefits the formulation of specific decarbonization strategies.

Research on Spatiotemporal Changes and Control Strategy of Carbon Emission in Shenyang

Scientific estimation and monitoring of regional long-term carbon emission change rules are the data support and scientific basis for developing differentiated emission reduction strategies. Based on the estimation data of energy carbon emissions from 2010 to 2021, DMSP/OLS and NPP/VIIRS lighting data, and the ESDA, Kaya identity, and LMDI models, the temporal and spatial changes and driving mechanism of carbon emissions in Shenyang were discussed. The results showed that: (1) During the study period, the carbon emission of energy consumption in Shenyang showed an upward trend, but the growth rate increased first and then decreased, and the carbon peak was not reached; (2) The spatial distribution of carbon emissions showed a radiative pattern decreasing from the center to the periphery; (3) The global Moran’s I of carbon emission is greater than zero, forming a high-high concentration distribution in the central region, low-low concentration distribution in the peripheral region, and low-high concentration distribution in the Yuhong region; (4) Economic development, population size, and energy efficiency are significant carbon-increasing factors, while industrial structure and energy structure factors are significant carbon-reducing factors. The order of driving factors is as follows: industrial structure > economic development > energy efficiency > population size > energy structure.

Implication of electrification and power decarbonization in low-carbon transition pathways for China, the U.S. and the EU

Electrification and power decarbonization are essential components of low-carbon energy transition, however, the combined effects of these two factors on CO2 emissions have not been comprehensively understood, especially with decomposition analyses for key regions. We present an extended Kaya identity to explicitly express power decarbonization (ACI) and electrification (Re) factors and analyze their combined effects on CO2 emissions in China, the U.S., and the EU between 1997 and 2020 using Logarithmic Mean Divisia Index (LMDI) model. We further develop a scenario framework to analyze the required Re and ACI to achieve specific climate targets in 2030, as well as emission levels from various scenarios of these two factors. The results indicate that the combined effects of electrification and power decarbonization played an important role in carbon-mitigation in China, the U.S., and the EU during 1997–2020, driving down CO2 emissions respectively by 0.9, 0.5, and 0.4 Gt during 1997–2007, by 2.6, 0.9, and 0.5 Gt during 2007–2017, and by 0.6, 0.4, and 0.3 Gt during 2017–2020. Further decomposition analyses reveal heterogeneity in the driving factors of electrification and power decarbonization evolutions across the three regions. Climate target scenarios signify that features of historical energy transition pathways may be insufficient for the U.S. and the EU to achieve their 2030 emission targets aligned with the 1.5 °C pathways. Suggestions on accelerating electrification process in those two regions toward their 2030 climate targets are proposed from the decomposition and scenario analyses' result.

Research on Influencing Factors of Residential Building Carbon Emissions and Carbon Peak: A Case of Henan Province in China

Buildings are considered to have significant emission reduction potential. Residential building carbon emissions, as the most significant type of building-related carbon emissions, represent a crucial factor in achieving both carbon peak and carbon neutrality targets for China. Based on carbon emission data from Henan Province, a large province located in central China, between 2010 and 2020, this study employed the Kaya-LMDI decomposition method to analyze seven driving factors of carbon emission evolution, encompassing energy, population, and income, and assessed the historical reduction in CO2 emissions from residential buildings. Then, by integrating Kaya identity static analysis with Monte Carlo dynamic simulation, various scenarios were established to infer the future evolution trend, peak time, and potential for carbon emission reduction in residential buildings. The analysis results are as follows: (1) The carbon emissions of residential buildings in Henan exhibited a rising trend from 2010 to 2020, albeit with a decelerating growth rate. (2) Per capita household disposable income is the main driving factor for the increase in carbon emissions, but the household housing purchase index inhibits most of the growth of carbon emissions for the residential buildings in Henan, with the total carbon emission reduction of residential buildings reaches 106.42 million tons of CO2 during the research period. (3) During the period from 2020 to 2050, residential buildings in Henan Province will exhibit an “inverted U-shaped” trend in carbon emissions under the three static scenarios. The base scenario predicts that carbon emissions will reach their peak of 131.66 million tons in 2036, while the low-carbon scenario forecasts a peak of 998.8 million tons in 2030 and the high-carbon scenario projects a peak of 138.65 million tonnes in 2041. (4) Under the dynamic simulation scenario, it is anticipated that residential buildings in Henan Province will reach their carbon peak in 2036 ± 3 years, with a corresponding carbon emission of 155.34 million tons. This study can serve as a valuable reference for the future development of low-carbon pathways within the building sector.

Study on the Relationship between Economic Growth of Animal Husbandry and Carbon Emission Based on Logarithmic Average Index Method and Decoupling Model: A Case Study of Heilongjiang Province

With the establishment of the action plan for the goal of “carbon peaking and carbon neutrality”, how to achieve high-quality agricultural development, help implement the construction of the green Longjiang River, reduce agricultural carbon emissions, and increase the level of agricultural carbon sink is a key problem that must be solved for Heilongjiang Province to achieve the goal of “double carbon”. This article uses the Life Cycle Assessment (LCA) method to estimate the carbon emissions of animal husbandry in Heilongjiang Province and 13 cities from 2000 to 2020. By constructing the Tapio decoupling model, Kaya identity, and the LMDI model, the relationship between animal husbandry economy and carbon emissions in Heilongjiang Province and the driving factors affecting animal husbandry carbon emissions are explored. The results indicate that: (1) From 2000 to 2020, the carbon emissions of animal husbandry in Heilongjiang Province showed an overall slightly upward trend. From the perspective of various emission links, the highest carbon emissions are from the gastrointestinal fermentation environment (42.49%), with beef cattle, cows, and live pigs being the main livestock and poultry in Heilongjiang Province with carbon emissions. (2) The Tapio decoupling model results indicated that from 2000 to 2020, the relationship between the economic development of animal husbandry in Heilongjiang Province and carbon emissions was mainly characterized by weak decoupling. (3) The main driving force behind the continuous increase in carbon emissions from animal husbandry in Heilongjiang Province is the changing factors of agricultural population returns and changes in the production structure of animal husbandry; The driving factors that suppress the increase in carbon emissions from animal husbandry in Heilongjiang Province are changes in animal husbandry production efficiency, population and urban development levels, and population mobility factors. Finally, based on the decoupling effect status and driving factors of decomposition between Heilongjiang Province and its various cities, it is recommended to implement countermeasures and suggestions for the transformation of animal husbandry in the province towards green and low carbon at the macro level. This can be achieved through the adoption of sustainable and eco-friendly practices such as the use of renewable energy sources and the reduction of greenhouse gas emissions. Additionally, promoting research and development in sustainable agriculture and animal husbandry can also contribute to the transformation towards a more environmentally friendly industry.

Dynamic analysis of correlation patterns between urban population and construction land at different administrative levels: The case of Hangzhou megacity

Understanding the urban human-land interaction at multiple administrative levels comprehensively plays an important role in the refined governance and thus provides critical references for urban planning and management. This paper analyzes the relationship between urban population and urban construction land in Hangzhou from perspectives of equilibrium, intensification and coordination at the multi-level during 2010 and 2020. Meanwhile, the underlying mechanism among the urban construction land expansion, population flow and land utilization efficiency with different demographic migration pattern is defined by Kaya identity and further quantitatively deconstructed by the Logarithmic Mean Divisia Index (LMDI). Regraded with the multi-level evaluation of human-land interactions, the prefectural-level analysis reveals significant reduction in both internal difference and absolute value of per capita urban construction land area, and the growth rate of urban population is 2.24 times faster than that of the urban construction land. At the county-level, the proportion of balanced and intensive county-level units increase by 15.38% and 38.46% respectively. The proportion of the county-level units under coordinated status reaches up to 92.31%. At the township-level, the proportion of intensive township-level units increases by 16.84% and the proportion of the township-level units under coordinated status is up to 64.74%. Furthermore, analysis of the multidimensional combination characteristics of urban human-land interaction is performed with six combination types at the county-level and four combination types at the township-level, which are useful for managers to develop categorized and refined urban development guidance. The driving mechanisms demonstrated that 47% of the townships with net population inflow and 41% of the townships with net population outflow are mainly influenced by the increase of permanent resident population, while the deterioration degree of land utilization efficiency in the former is significantly lower than that in the latter. This result implies that the townships in the core urban areas present the better performance in the improvement of land utilization efficiency than those in the western areas of Hangzhou. The exploration will provide references for coordinating the rational allocation of land resources and optimizing the regional human-land relationship.

Probability of Achieving NDC and Implications for Climate Policy: CO-STIRPAT Approach

<hr /> <p><big>This paper presents an analytical framework to assess the probability of achieving nationally determined contributions (NDC). The prediction model based on the Kaya identity is used to simulate the pathway of carbon emission until the target year. Applying the modified STIRPAT framework (named CO-STIRPAT) to data observed in South Korea shows that the probability that the predicted pathway with existing climate technology will stay above the NDC target pathway is significantly high. The result suggests that it is necessary to design a climate policy to improve energy intensity and carbon intensity by accelerating the advance in climate technology.</big></p>

Environmental impact of economic activities: Decoupling perspective of Singapore using log mean Divisia index decomposition technique

The chase for economic growth results in global environmental degradation, threatening the socioeconomic aspects of human lives. Singapore is a global economic player, transforming its rural setup into an urban structure to achieve higher economic growth (EG). However, the drive for EG drastically affected its environmental quality. In this respect, the present study analyzes the relationship between Singapore's economic activities and environmental quality. This study uses the Tapio decoupling indicator, Kaya Identity, and the Log Mean Divisia Index (LMDI) decomposition techniques to assess the relationships between these paramount factors from 1990 to 2016. The LMDI analysis reveals that EG and population are the main contributors to carbon emissions (CE), whereas carbon intensity reduces the environmental impact. However, energy intensity and energy structure have depicted mixed effects on CE. Further, Tapio analysis reveals that Singapore has experienced strong decoupling (SD) in most study years. Additionally, expensive negative decoupling (END), weak decoupling (WD), and strong negative decoupling (SND) were also observed during the study period. An expanded decomposition analysis reveals that population and EG deteriorate environmental quality in Singapore. While carbon intensity is the critical driver that strengthens the decoupling progress, energy intensity and structure depict a mixed effect on the decoupling process.

Technological pathways to decarbonisation and the role of renewable energy: A study of European countries using consumption-based metrics

To decarbonise Europe in the post-COP26 era, current policies must be adjusted to account for the cross-border consequences of its consumption pattern. Using consumption-based Kaya identity metrics adjusted for emissions and energy embodied in traded goods and services, this study examines the nexus between technological factors defining energy transition progress, specifically energy and carbon intensities of the consumption mix, and carbon dioxide (CO2) emissions, in a sample of 20 European countries from 1995 to 2019. The empirical steps rely on panel-data estimators that are robust to cross-sectional dependence and allow for heterogeneous slope coefficients. The results show that energy and carbon intensities of the consumption mix have a positive relationship with CO2 emissions but a negative relationship with renewable energy consumption. The findings also verify an inverted U-shaped relationship between affluence and CO2 emissions through the energy and carbon intensity metrics. Additional tests show a unidirectional causality from carbon intensity of the energy mix to CO2 emissions and from renewable energy to the carbon intensity. Also, bidirectional causality exists between CO2 emissions and per capita GDP and energy intensity, and between renewable energy and energy intensity. By implication, renewable energy provides the technological path to mitigating consumption-induced emissions in Europe.

Spatio-temporal variation and influencing factors of industrial carbon emission effect in China based on water-land-energy-carbon nexus

The industrial production requires the input of water, land and energy, and the water-land-energy interaction generates carbon emissions. Investigating the industrial water-land-energy-carbon nexus and exploring the influencing factors of the carbon emissions help promote the intensive use of water resources, land resources, and energy, as well as the low-carbon development of industry. This paper applied kernel density estimation and spatial auto-correlation analysis to explore the spatio-temporal variation of industrial carbon emission effect of water and land use and the industrial matching status of water and land resources in China, and then analyzes the influencing factors to industrial carbon emissions by introducing water and land resource factor into Kaya identity and Logarithmic Mean Divisia Index (LMDI) model. The main conclusions are: (1) From 2006 to 2020, China's industrial carbon emissions increased by 82.02%. The industrial carbon emission intensity of water use (CEIWU) and the industrial carbon emission intensity of land use (CEILU) increased by 158.02% and 55.53% respectively. The inter-provincial difference of CEIWU and CEILU expanded considerably, and the number of provinces with high CEILU had a large increase. (2) The industrial matching index of water and land resources (MIWL) in China showed a fluctuating downward trend, presenting great spatial clustering, and the provincial MIWL were mainly concentrated in the H-H and L-L regions. (3) The contributing and inhibitory effect of each influencing factor varied on regional and provincial level. The economic output of water use is a contributing factor to all regions and provinces, while the MIWL is an inhibitory factor to all regions and provinces.

The estimation of carbon imbalance and driving factors in China's urban residential building sector

Understanding the imbalance of carbon emissions in the urban residential building (URB) sector is beneficial for equitable and effective emission reduction policies. However, carbon imbalance in URB and its major driving factors remain unclear. Therefore, according to the Kaya identity and Zenga index, this study aims to analyze the imbalance in carbon emissions and carbon emission unit area of URB from 2005 to 2019. The results represent the following: 1) Although the overall carbon emission unit area reached its peak value (36.17 kgCO2/m2) in 2011, the overall carbon emission of URB did not reach the peak value, arriving at 0.86 BtCO2 in 2019; 2) the obvious imbalance of carbon emission and carbon emission unit area was led by the population and energy consumption unit area, respectively; 3) Compared to the difference in economy, the difference in climate had a larger impact on inter-group imbalance of carbon emission unit area without heating. In summary, these results and provided policies facilitate future formulation of fair and effective provincial decarbonization responsibility and emission mitigation implementation policies.

Decoupling Analysis of Energy-Related Carbon Dioxide Emissions from Economic Growth in Poland

This paper addresses the conditions behind energy-related carbon dioxide (CO2) emissions in Poland following its accession to the European Union. The study analyzed some energy, fossil fuel, economic, and environmental indicators, such as energy use, CO2 emissions, driving factors, decoupling elasticity status, and decoupling effort status. It relied on the Kaya identity and Logarithmic Mean Divisia Index (LMDI) in determining the drivers of CO2 emissions. As shown by the results, between 2004 and 2020, energy consumption in Poland grew at an average annual rate of 0.8%, while fossil fuel carbon emissions declined at 0.7% per year. Energy intensity was found to be the key force behind the reduction in CO2 emissions, whereas rapid economic growth was the main driver of CO2 emissions. While other factors, i.e., carbon intensity, energy mix, and population, also contributed to reducing the emissions, they had a much smaller—if not marginal—effect. In turn, the decoupling elasticity analysis showed that Poland mostly witnessed strong decoupling states, which means a strong decoupling of CO2 emissions from economic growth. Furthermore, it follows from the decoupling effort analysis that strong decoupling effort statuses prevailed throughout the study period, suggesting that the changes in the considered factors significantly contributed to reducing CO2 emissions. However, both the decoupling elasticity indices and the decoupling effort indices revealed the presence of an adverse trend. The above means that Poland witnessed a decline both in the strength of decoupling CO2 emissions from economic growth and in the efficiency of policies and measures designed to reduce CO2 emissions.

Empirics on linking industrial agglomeration, energy consumption, residential construction sector growth, and environmental sustainability.

We looked at the long-term and short-term diversifiedrelationships between industrial agglomeration, aggregate energy consumption, residential construction sector growth, and air pollution in China's 30 provincial units from 2004 through 2020. We contributed to the existing knowledge by calculating a holistic air pollution index (API) and applying advanced methods. We also augmented the Kaya identity by including industrial agglomeration and residential construction sector growth in the baseline framework. Based on empirical results: First, we revealed long-term stability among our covariates through panel cointegration analysis. Second, we uncovered a positive bilateral relationship between residential construction sector growth and industrial agglomeration in the long and short term. Third, we unfolded a unilateral positive correlation emerging from aggregate energy consumption to API, displaying the greatest influence in the east zone of China. Fourth, we observed a unilateral positive connection stemming from industrial agglomeration and residential construction sector growth to aggregate energy consumption and API in the long- and short-term dimensions. Finally, the linking nature was homogeneously prevailing across the long term and short term; however, the long-term impact size outweighed that of the short term. Given our empirical results, useful policy insights are discussed to provide the readers with a take-home message for substantiating sustainable development goals.

Assessing the dynamic land utilization efficiency and relevant driving mechanism in in-situ urbanized rural areas: A case study of 1979 administrative villages in Hangzhou

Rural revitalization is inseparable from the coordinated human-land relationship, and population-oriented land utilization efficiency is the most important manifestation of human-land interaction. Leveraging the diverse demographic migration patterns and adequate micro-scale units in in-situ urbanized rural areas, we firstly propose a dynamic land utilization efficiency indicator, which characterizes the intensification and coordination degree of human-land coupling simultaneously. Meanwhile, the intrinsic correlation among the rural residential land expansion, population flow and land utilization efficiency from the perspective of demographic migration is defined by Kaya identity and further quantitatively deconstructed by the Logarithmic Mean Divisia Index. The results demonstrate: only 24.4% of the villages are of intensive land utilization. The decreased land utilization efficiency under different demographic migration patterns represents similar proportions and differentiated intensities. It is found to occur in nearly 60% of both the villages with net population inflow and the villages with net population outflow. And the deterioration degree of land utilization efficiency in the former is generally significantly higher than that in the latter. While the villages with net population inflow present the better performance in the improvement of land utilization efficiency than that in the villages with net population outflow. The exploration will benefit rural sustainable development and provide effective inspiration for policymakers in rural revitalization.

Decomposition of carbon emission driving factors and judgment of peak status in countries along the Belt and Road

Most of the countries along the Belt and Road are still developing, with their carbon emissions yet to peak. There is a lack of comprehensive analysis and research to judge these countries' current carbon peak state and quantify key driving factors contributing to their carbon emissions. This study aims to fill this gap.A new method for judging a country's peak carbon status based on a time series of carbon emissions is developed. We divide the status of all countries along the Belt and Road into four categories: reached the peak, peak plateau period 1 (the downward trend is not significant), peak plateau period 2 (obvious recession), and not reached the peak. LMDI factorization is used to decompose the change in carbon emissions of energy consumption into multiple factors: carbon intensity, energy intensity, economic output, and population size, based on Kaya's identity theory. The carbon emission and socioeconomic databases from 2000 to 2019 are utilized for this analysis. The main positive driving factor of the three countries (Hungary, Romania, Czech Republic) that have reached the peak is GDP PPP per population, while other driving factors make negative contributions to carbon emissions. In some years, these countries briefly experienced a negative contribution of GDP PPP per population to carbon emissions. The driving factors of carbon emissions for countries in the peak plateau period are not stable, with contributions of GDP PPP per population, energy intensity, and carbon intensity fluctuating periodically. In countries that have not reached the peak of carbon emissions, population growth and economic growth are significant positive contributors, while the effect of driving factors that negatively contribute to carbon emissions is less obvious.The study's findings provide valuable insights into the carbon emission peak status and driving factors of countries along the Belt and Road, which can be used to guide policymaking and future research in addressing climate change and promoting sustainable development in these regions.