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The Chinese government has explicitly promised to peak carbon dioxide emissions by 2030 and strive to become carbon neutral by 2060. As the capital of China, Beijing should play a pilot role in reducing carbon emissions. Researching on the synergistic effect of air pollutants and carbon dioxide emissions reduction can be conducive to the reduction of carbon and pollution, and ultimately promote economic growth and enhance environmental management. Based on the extended Kaya identity and the gray correlation model, this study analyzes the correlation degree of the influencing factors of collaborative emission reduction. The Logarithmic Mean Divisia Index method (LMDI model) is conducted to decompose the driving effects and quantify the collaborative emission reduction effects of main air pollutants and carbon dioxide in Beijing. The results showed a strong correlation (correlation coefficient > 0.6) between carbon dioxide and major air pollution. The energy intensity and energy structure are the main factors to promote the major air pollutants emission reduction in Beijing, while the economic output and population size increase the air pollutant emissions. The average CO2 contribution rate to SO2, NOx, and PM10 from 2010 to 2019 was 9.60, 5.99 and 9.06%, respectively. In general, there is a significant connection between CO2 emissions and the main air pollutants. However, the synergistic emission reduction effect of CO2 and SO2 is greater than that of CO2 and NOx, and CO2 and PM10. Finally, this paper proposes several countermeasures and suggestions for front-end prevention, middle-end control, and collaborative emission reduction based on the findings. [ABSTRACT FROM AUTHOR]

[Display omitted] • Factors of CO 2 emissions are examined by Kaya-LMDI analysis in China. • Tapio method is used to determine the decoupling elasticity. • Reductions of energy and carbon intensity decrease carbon emissions' growth. • Carbon emissions and the economy are weakly decoupled currently. The main aim of this study is to investigate how the different drivers regulating incremental CO 2 emissions have evolved in China over seven five-year plans (1991–2022). The study also examines how these different drivers' (including population, economic development, energy intensity, share of renewable energy and its productivity, and carbon intensity) regulating of CO 2 emissions have changed. The results are obtained by employing Kaya identity and logarithmic mean divisia index (LMDI) methodology. Additionally, the Tapio method is utilized to study the decoupling elasticity history of carbon emissions from China's economy. The decoupling of carbon emissions from the economy due to different drivers is further investigated, and the results may inform and assist policymakers in determining environmentally sustainable policies. The results reveal that the value added of CO 2 emissions reached its highest point in the 10th five-year plan (FYP) and has decreased every year thereafter. Economic factor and renewable energy share factor are the main reasons for the increase in carbon emissions. Those emissions peaked in the 11th and 12th FYPs, respectively, and have been gradually declining in subsequent years. Meanwhile, the energy intensity factor, the renewable energy utilization factor, and the carbon emission intensity factor, as the key contributors to carbon emission reduction, have made monumental contributions to carbon emission reduction. These factors reached their highest levels in the 12th and 13th FYPs, respectively. China was still in a stage of weak decoupling at this stage, but the decoupling elasticity is gradually converging to zero. On an individual basis, energy intensity, carbon intensity, renewable energy productivity, and population factor have a much greater ability to decouple than economic factor and renewable energy share factor. Some implications are offered, including the improvement of population quality, the promotion of a green and low-carbon economy, and the innovation of energy efficiency. [ABSTRACT FROM AUTHOR]

Water withdrawals are expected to continue growing, further straining available freshwater resources for economies. This review focuses on two key relationships between water and economic growth for developed and developing countries. Rising freshwater withdrawals and stress are directly related to how our economies use water to develop and grow. There is also concern that increasing water scarcity will impact economic growth across countries and regions. Unless there is a marked improvement in the water intensity of countries, economic growth will be the most important driver of increasing water withdrawals and scarcity. Although analysis of the impact of freshwater stress on the growth of economies is inconclusive, there is mounting evidence of an impact at the river basin and sub-national level. Decoupling rising water use from growth will require major water management reforms. Two of them are discussed here: Ending excessive subsidization of irrigation and reallocating subsidies for water supply and sanitation to expand delivery in developing countries. [ABSTRACT FROM AUTHOR]

The far-reaching effects of climate change on the environment are particularly pronounced in developing countries, with the African continent facing the highest risks. Ironically, there is a scarcity of empirical research addressing the perspectives of African nations regarding climate change mitigation strategies. In alignment with the ''African We Want'' agenda, this study investigates energy efficiency within the KAYA identity framework as a strategic pathway toward a greener Africa. The empirical findings, derived from feasible generalized least squares (FGLS) and panel-corrected standard errors (PCSE), indicate that Africa's current energy and carbon intensity profiles are detrimental to sustainable growth. The results reveal a heavy reliance on traditional energy sources rather than cleaner alternatives. The validation of the Environmental Kuznets Curve (EKC) hypothesis suggests that economic activities could contribute to cleaner environments in the long term. While eco-complexities and population growth are significant drivers of pollution, the role of ICT has shown substantial climate resilience effects. As a policy recommendation, the continent must reduce its dependence on traditional energy sources and shift towards more environmentally friendly modern energy options. Embracing modern manufacturing techniques and facilitating economic transformations will be crucial for achieving the continent's climate change resilience goals by 2060.

This paper discussed the impact of economic development on carbon emissions in Sichuan Province from 1990 to 2020 and put forward some suggestions to accelerate the development of a low-carbon economy. Firstly, the land use type was reclassified, then the IPCC coefficient method was used to calculate the land use carbon emissions, and then the driving factors affecting the carbon emissions were obtained by the log-average factor index (LMDI) method. Finally, the decoupling elasticity index and its influencing factors were obtained using Tapio decoupling model. The results showed that: (1) Carbon emissions in Sichuan Province increased significantly, with a growth rate of 218.7% from 1990 to 2020. Per capita GDP, land use structure and land use intensity per unit GDP were the main reasons for carbon emissions change. (2) Sichuan Province achieved strong decoupling during 1995-2000 and 2015-2020. However, it was in a state of expanding connection during 2000-2005, and the economic pressure on the environment reached the maximum. (3) Per capita GDP was the main factor promoting the elastic growth of decoupling index, while land use intensity per unit GDP is the main reason inhibiting the elastic growth of decoupling index. [ABSTRACT FROM AUTHOR]

Energy consumption is a crucial driver for economic development, improving the quality of life of the population of a country. This study attempts to contribute to the discussion by employing a systemic approach and methodology to examine the relationship between energy consumption (EC), gross domestic product (GDP) and carbon dioxide emissions (CO2) in Ecuador using time series from 1990 to 2018 with a mixed methodology (quantitative and qualitative). The energy balance and the enlarged Kaya identity are utilised to quantify the environmental impact of human activities. Furthermore, correlational cointegration and Granger causality tests are used to analyse the long-term and short-term relationships between variables in different sectors. The results reveal that there is no Granger causality between the variables in the agriculture and transport sectors, but there are unidirectional causality relationships in the industry and services sectors. In the industry sector, the study finds that EC Granger causes GDP (Wald test p value = 0.0038) and CO2 Granger causes GDP (Wald test p value = 0.0433). In the services sector, GDP Granger causes CO2 (Wald test p value = 0.0075), and EC Granger causes CO2 (Wald test p value = 0.0122), reinforcing the loop between GDP and CO2 in both the sectors. The analysed relationships help to inform policymakers about the likely impact of interventions. In addition, the study shows that Ecuador is in the initial phase of the Environmental Kuznets Curve, and provides strategies to manage sectoral energy consumption and valuable insights for other developing countries in Latin America seeking to pursue sustainable development. [ABSTRACT FROM AUTHOR]

[Display omitted] • Utilized a novel Method of Moments Panel Quantile Regression. • Decentralized governance/ natural resources directly impedes ecological quality. • Decentralized expenditures promote ecological sustainability for inclusive finance. • Financial inclusion induces ecological sustainability effects of natural resources. • R&D expenditure improves ecological sustainability. While natural resource consumption is critical for almost all production processes, the overdependence on and poor governance of those resources might result not only in natural resource depletion but also in ecological unsustainability. Against this background, the present research explores the novel perspective on how financial inclusivity moderates the effects of decentralized governance systems and natural resource reliance on ecological sustainability in the presence of research and development (R&D) expenditures. This research employs a novel method of moments quantile regression on data from eight selected OECD countries during 1995–2020. It is found that decentralized governance systems deteriorate ecological sustainability across all quantiles, with a more substantial impact for higher quantiles of ecological intensity. Natural resource reliance also hinders ecological sustainability, with the degree of effects decreasing from lower to higher quantiles of ecological intensity. Financial inclusion directly mitigates ecological unsustainability, manifesting a more powerful influence in ecologically more intensive countries. Concerning moderation, financial inclusivity negatively moderates the influence of decentralized governance systems and natural resource reliance on ecological intensity, showing stronger relationships in ecologically more intensive countries. Taking the other covariates into account, the EKC curve is uncovered to exist for all levels of ecological sustainability. Moreover, the labor force participation rate exerts ecological pressures, especially for countries with low ecological sustainability. Besides, R&D expenditures are negatively associated with ecological intensity and are responsible for the betterment of the OECD's ecological sustainability. The baseline findings are robust to those of additional models employing ecological footprint as an alternative dependent variable. Findings implicate that subnational governments should promote (i) green microfinancing to resource-efficient investments, (ii) funding to small businesses extending sustainable business solutions, (iii) green finance thinking among the general public, and (iv) financing to localized sustainability projects. [ABSTRACT FROM AUTHOR]

The overwhelming effects of climate change on the living environments has prompted several countries, including India into rolling out different carbon neutrality agenda. On this background, this study activated policy framework towards the attainment of India’s 2070 net-zero emission target via energy efficiency. The roles of green-technology, affluence and population were also rectified. With quarterly series spanning 1997Q1–2021Q4 and estimates of the novel wavelet quantile correlation technique, the following insights sufficed. Energy-intensity generated significant carbon de-escalation effects mainly in the medium and long term. There were evidence of long-term asymmetric effects between them. Carbon intensity as well as green technology aggravated carbon emissions in both short and medium term, however, over the long term, they generated carbon neutrality effects. The empirical estimates also validated the environmental Kuznets curve (EKC) ideology given the long-term environmental quality enhancing attributes of GDP. India’s population represents a major challenge for the net-zero emission target. But this can be curtailed through adequate orientations as prescribed in the LiFE progrmme. Among other considerations, India’s net-zero target is realisable if the country extends its low-carbon energy profiles and deploy more green technologies.

This study models the Kaya identity equation for carbon dioxide (CO2) emissions in a panel of 20 oil-rich countries from 1994 to 2019. The estimators used are robust to cross-sectional dependence and allow for heterogeneous slope coefficients. The results indicate that natural resource extraction hinders environmental sustainability in oil-rich countries by altering the structural composition of their consumption mix towards energy- and carbon-intensive technologies. However, this relationship is only significant after reaching a turning point level of resource extraction. This suggests that the carbon curse is only triggered at higher levels of resource dependence, supporting a U-shaped relationship between natural resource extraction and CO2 emissions. The threshold for the natural rents to GDP ratio, beyond which natural resource extraction triggers the carbon curse, is found to be 12.18 %. The vulnerability assessment reveals that 17 countries in the panel, including Algeria, Kazakhstan, the United Arab Emirates, Iran, Iraq, Kuwait, Qatar, Oman, Saudi Arabia, the Congo Republic, and Libya, are already within the carbon curse zone. From a policy perspective, promoting sustainable development in oil-rich economies requires a shift towards renewable energy sources, reducing reliance on fossil fuels, and widespread adoption of energy efficiency and conservation mechanisms.

The overwhelming effects of climate change on the living environments has prompted several countries, including India into rolling out different carbon neutrality agenda. On this background, this study activated policy framework towards the attainment of India's 2070 net-zero emission target via energy efficiency. The roles of green-technology, affluence and population were also rectified. With quarterly series spanning 1997Q1–2021Q4 and estimates of the novel wavelet quantile correlation technique, the following insights sufficed. Energy-intensity generated significant carbon de-escalation effects mainly in the medium and long term. There were evidence of long-term asymmetric effects between them. Carbon intensity as well as green technology aggravated carbon emissions in both short and medium term, however, over the long term, they generated carbon neutrality effects. The empirical estimates also validated the environmental Kuznets curve (EKC) ideology given the long-term environmental quality enhancing attributes of GDP. India's population represents a major challenge for the net-zero emission target. But this can be curtailed through adequate orientations as prescribed in the LiFE progrmme. Among other considerations, India's net-zero target is realisable if the country extends its low-carbon energy profiles and deploy more green technologies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Investigating links between water, energy, and carbon emissions requires more attention on the path toward economic prosperity. This study aims to develop a framework for modeling water-energy-carbon interdependencies by considering the nonlinear relationships in their dynamic feedback processes. The main contribution of this research is the quantification of the carbon footprint of industrial water use through the development of an Industrial Water-Energy-Carbon (I-WEC) nexus model. It is a system dynamics model that is developed with a scenario-driven framework. The GDP as a representative of economic growth is assessed. The proposed methodology is tested on the Netherlands' industrial sector as a pilot due to the relatively good data structure. Based on policy-based complementary scenarios, the results show a 3% increase in total water use by 2030. Energy use and carbon emissions will fall as much as 10% and 25% that year, respectively. It is concluded that the industrial GDP share could be maintained with a 0.76% loss, which is close to the 0.5% loss projected by authorities. This study presents a unique approach that can be used in other regions. [ABSTRACT FROM AUTHOR]

Global 21st century Kyoto Gas emissions growth as forecast in SSP2 (a middle-of-the-road future climate scenario) is largely driven by expected: (a) per-capita GDP growth; and (b) energy/non-CO 2 GDP intensity reduction. While models of the former have been comprehensively critiqued, the rationale for the latter has not. This paper uses a new consumption-based methodology to determine likely future emissions intensity reductions implicit in changing consumption patterns. Its analysis of household expenditure surveys, macroeconomic data and income elasticities inform a model of how future consumption pathways could evolve with different levels of national incomes to 2100. These pathways are then combined with existing emissions intensity data to quantify the implied impacts of consumption change on overall emissions intensity. Introducing such a consumption factor into established decomposition methodologies then allows demonstration of the scale of non-consumption intensity reductions required. Results suggest that emissions intensity peaks at poverty-like national income levels, where household/transport fuels dominate emissions. Thereafter, intensity reduces with national income growth, though absolute emissions continue to rise. We find that expected changes in consumption patterns will deliver less than half required consumption energy intensity reduction to meet SSP2-Baseline projections to 2100. Such implied non-consumption-pattern improvement requirements may appear relatively undemanding in total against historic performance, but for some regions and timescales this is not the case and the role of mitigation in the historic data may render a forecast baseline (where mitigation is excluded) optimistic. The paper's methodology and findings are relevant for inequality scholars, climate modellers, and governments and policymakers, helping them facilitate a better understanding of how consumption pathways interact with climate futures for whole economies and particular sectors within those. The impact of income-driven changes in consumption patterns on Kyoto gas emissions during the twenty-first century. Notes: 1Fossil fuel and industry emissions only, excludes land-use and other emissions. Red text represents increasing impact on emissions and green text represents reducing impact. Source: Authors' analysis. [Display omitted] • A new consumption-driven model for analysing global emissions growth in SSP2 baseline • Kyoto gas emissions per unit of GDP EAK AT C. $3000 GDP per capita (2005 US$ PPP) • Consumption evolution gives <50 % energy intensity drop implied in SSP2 baseline • Regional divergence in acceleration of energy intensity of consumption required [ABSTRACT FROM AUTHOR]

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.

Nowadays, a very strong concern is coming from the fact that human intervention is heavily affecting the environment. In the past, the most harmful countries for the environment were the USA and Europe due to their development and level of industrialization. Today, the most impactful countries on the environment are the ones from across Asia, especially China and India. In order to interrupt these issues and to help prevent the further deterioration of the world, the UN redacted the 2030 Agenda. This presents a possible way in which countries might act against the effects of climate changes, reducing global warming and further world pollution. Being the most ambitious in this regard, the EU decided to implement the Green Deal. In our paper, based on the EU accomplishments in this direction, we try to build a scenario of how the world will look like if the three most polluting countries will apply the targets set by the EU. In this attempt, we used the Kaya Identity to measure the forecasted impact and arrived to the conclusion that, by applying this measures, energy consumption will be reduced, the consumption of renewable energy will increase, CO2 emissions will be reduced and the world can manage to come back to the level it had in 1990. [ABSTRACT FROM AUTHOR]

There are two main ways to reduce anthropogenic GHG emissions: energy efficiency improvement and increase usage of renewable energy sources. Taking these two main ways into account, it is possible to analyze the main drivers of GHG emissions in the country and to make forecast of future GHG emissions based on historical trends. The Visegrad group (V4) countries, including Poland, Hungary, Slovakia, and Czech Republic were selected to provide comparative assessment of their GHG emission drivers and to evaluate effects of climate change mitigation policies in energy sector on GHG emission trends. The Kaya identity approach was applied allowing to perform simple multiplication. Kaya identity equation substitutes the factors with wellestablished and measurable quantities, which leave little space for ambiguity. The multiplying population size by GDP per capita, energy intensity, and carbon intensity of energy allows to get total GHG emissions in the country and define its energy efficiency or use of renewables are the main drivers of GHG emissions, including the effect of economic growth expressed by GDP per capita. [ABSTRACT FROM AUTHOR]

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.

China is currently in the process of the middle and late stage of industrialization, and energy-related CO2 emissions have reached 10 Gt. This paper models energy consumption and related CO2 emissions and analyzes the development trends of various driving factors of CO2 emissions, explicitly considering China's economic and social development goals in the medium- and long-term. Different scenarios for 2020–2060 are projected based on the Kaya identity. Energy consumption will peak at 6,200 Mtce in 2035 in the BAU scenario with the energy-related CO2 emissions peaking at 11.1 Gt in 2026–2027 advanced by the rising non-fossil energy share, while energy consumption and CO2 emissions in the accelerated transition scenario would be potentially reduced by 300 Mtce and 300–400 Mt respectively. In the BAU scenario, the net CO2 emissions will remain around 2,000 Mt in 2060, after removing 1,800 Mt by CCS/CCUS, indicating that China needs to enhance its post-2030 policy to deliver the carbon neutrality pledge. Steady energy efficiency improvement, radical industrial and energy restructuring are three key driving forces for carbon neutrality. [ABSTRACT FROM AUTHOR]

The economic loss caused by frequent flood disasters poses a great threat to China's economic prosperity. This study analyzes the driving factors of flood-related economic losses in China. We used the extended Kaya identity to establish a factor decomposition model and the logarithmic mean Divisia index decomposition method to identify five flood-related driving effects for economic loss: demographic effect, economic effect, flash flood disaster control effect, capital efficiency effect, and loss-rainfall effect. Among these factors, the flash flood disaster control effect most obviously reduced flood-related economic losses. Considering the weak foundation of flash flood disaster prevention and control in China, non-engineering measures for flash flood prevention and control have been implemented since 2010, achieving remarkable results. Influenced by these measures, the loss-rainfall effect also showed reduction output characteristics. The demographic, economic, and capital efficiency effects showed incremental effect characteristics. China's current economic growth leads to an increase in flood control pressure, thus explaining the incremental effect of the economic effect. This study discusses the relationship between flood-related economic loss and flash flood disaster prevention and control in China, adding value for the adjustment and formulation of future flood disaster prevention policies. [ABSTRACT FROM AUTHOR]

In response to shareholder interest in greater transparency, oil industry participants issued reassuring forecasts about the role of fossil-fuels by 2040. While differing in energy demand analysis, these forecasts had similar, status-quo predictions on energy intensity and fossil fuels' share of total energy. The forecasts, however, failed to consider the effects of their own alternative energy projects as well as four key contributors to oil market instability; (1) responses to global warming, (2) mandates to write off noneconomic reserves, (3) oligopolistic oil pricing, and (4) technological change. All of these instabilities converged in 2020, causing industry forecasts to be outdated. [ABSTRACT FROM AUTHOR]

Economic growth has historically been the main driver of rising greenhouse gas (GHG) emissions. To achieve steep emission reductions, the world would have to either decouple global GHG emissions from gross domestic product (GDP) at an unprecedented pace or face deep cuts to GDP. The so-called 'green growth' literature is optimistic that suitable policies and technology can enable such fast decoupling, while 'degrowth' proponents dismiss this and argue that the global economy must be scaled down, and that systemic change and redistribution is necessary to accomplish this. We use the so-called Kaya identity to offer a simple quantitative assessment of the gap between the historic performance in reducing the emission intensity of GDP and what is required for green growth, i.e., the basis of ongoing disagreement. We then review the literature on both degrowth and green growth and discuss their most important arguments and proposals. Degrowth authors are right to point out the considerable gap between current climate mitigation efforts and what is needed, as well as the various technological uncertainties and risks such as rebound effects. However, the often radical degrowth proposals also suffer from many uncertainties and risks. Most importantly, it is very unlikely that alternative welfare conceptions can convince a critical mass of countries to go along with a degrowth agenda. Governments should therefore instead focus on mobilizing the necessary investments, pricing carbon emissions, and encouraging innovation and behavioral change. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions from agriculture contribute to climate change. The consequences of unsustainable agricultural activity are polluted water, soil, air, and food. The agricultural sector has become one of the major contributors to global GHG emissions and is the world’s second largest emitter after the energy sector, which includes emissions from power generation and transport. Latvian and Lithuanian agriculture generates about one fifth of GHG emissions, while Estonia generates only about one tenth of the country’s GHG emissions. This paper investigates the GHG trends in agriculture from 1995 to 2019 and the driving forces of changes in GHG emissions from the agricultural sectors in the Baltic States (Lithuania, Latvia, and Estonia), which are helpful for formulating effective carbon reduction policies and strategies. The impact factors have on GHG emissions was analysed by using the Logarithmic Mean Divisia Index (LMDI) method based on Kaya identity. The aim of this study is to assess the dynamics of GHG emissions in agriculture and to identify the factors that have had the greatest impact on emissions. The analysis of the research data showed that in all three Baltic States GHG emissions from agriculture from 1995 to 2001–2002 decreased but later exceeded the level of 1995 (except for Lithuania). The analysis of the research data also revealed that the pollution caused by animal husbandry activities decreased. GHG intensity declined by 2–3% annually, but the structure of agriculture remained relatively stable. The decomposition of GHG emissions in agriculture showed very large temporary changes in the analysed factors and the agriculture of the Baltic States. GHG emissions are mainly increased by pollution due to the growing economy of the sector, and their decrease is mainly influenced by two factors—the decrease in the number of people employed in the agriculture sector and the decreasing intensity of GHGs in agriculture. The dependence of the result on the factors used for the decomposition analysis was investigated by the method of multivariate regression analysis. Regression analysis showed that the highest coefficient of determination (R2 = 0.93) was obtained for Estonian data and the lowest (R2 = 0.54) for Lithuanian data. In the case of Estonia, all factors were statistically significant; in the case of Latvia and Lithuania, one of the factors was statistically insignificant. The identified GHG emission factors allowed us to submit our insights for the reduction of emissions in the agriculture of the Baltic States.

This study models the Kaya identity equation for carbon dioxide (CO 2 ) emissions in a panel of 20 oil-rich countries from 1994 to 2019. The estimators used are robust to cross-sectional dependence and allow for heterogeneous slope coefficients. The results indicate that natural resource extraction hinders environmental sustainability in oil-rich countries by altering the structural composition of their consumption mix towards energy- and carbon-intensive technologies. However, this relationship is only significant after reaching a turning point level of resource extraction. This suggests that the carbon curse is only triggered at higher levels of resource dependence, supporting a U-shaped relationship between natural resource extraction and CO 2 emissions. The threshold for the natural rents to GDP ratio, beyond which natural resource extraction triggers the carbon curse, is found to be 12.18 %. The vulnerability assessment reveals that 17 countries in the panel, including Algeria, Kazakhstan, the United Arab Emirates, Iran, Iraq, Kuwait, Qatar, Oman, Saudi Arabia, the Congo Republic, and Libya, are already within the carbon curse zone. From a policy perspective, promoting sustainable development in oil-rich economies requires a shift towards renewable energy sources, reducing reliance on fossil fuels, and widespread adoption of energy efficiency and conservation mechanisms., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Clustering the manufacturing industries nearby major river is a common feature globally and can offer significant benefits in terms of productivity improvement. Nonetheless, such clustering also poses extensive and far-reaching environmental burdens on the river. China is implementing a green and low-carbon transformation strategy along the Yangtze and Yellow Rivers, two of the largest rivers in its manufacturing hub. This research examines the following three ways for decoupling economic growth and CO 2 emissions in a typical industrial park located along the Yangtze River, which includes the chemical industry, textile industry, glass manufacturing industry, and automobile manufacturing as pillar industries. First, we use the Tapio decoupling model and Kaya identity to forecast the decoupling paradigm between economic growth and CO 2 emissions in the park by 2030 in response to policy interventions. Second, 10 two-digit level industries (TDLIs) with high energy-resource consumption and CO 2 emissions in the manufacturing sector are evaluated. Third, a model is developed to determine the most appropriate approach for modifying the 10 TDLIs' structures under different scenario analysis. The main findings are as follows. It is expected that the parks could achieve a relative decoupling around 2028 and 2029 under policy interventions. In addition, tailored policies for industries with high economic output and low CO 2 emissions are proposed, while their opposites are also. Lastly, the optimal way for the park to decouple economic development and CO 2 emissions is to simultaneously adjust its industrial structure and upgrade its energy facilities. This would result in a 31% reduction in CO 2 emissions with positive effects on the economy and continuous upgrading of the industrial structure of the park. This study will play an important role in the green transformation of China's manufacturing clusters located along the Yangtze and the Yellow Rivers and will also have implications for other similar manufacturing clusters. • Reduction of carbon dioxide (CO 2) in industrial parks near big rivers is revealed. • A 3-model integrated method decouples economic growth from CO 2 emissions in parks. • CO 2 emissions quota, economic growth, and adjustment smoothness are contemplated. • Industrial structure adjustment boosts economic growth and reduces CO 2 emissions. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is one of the most reliable ways toward carbon neutrality. Most previous studies have focused on how to reduce energy intensity; however, it is not enough to provide an overview of how many carbon emissions can be reduced by technical efficiency improvements underlying energy systems. To fill this gap, this study extended the common Kaya identity to systematically evaluate the carbon reduction potential from technical efficiency improvements of various technical conversion components within the energy system at a granular level. The extended Kaya identity includes technical efficiency factors of electricity efficiency, conversion efficiency, and passive efficiency. It provides a comprehensive framework to evaluate current performance, historical contributions, and future potential of technical efficiency improvements. The case of China reveals that: Currently, only around 5% of energy sources were delivered to final services. By improving technical efficiency to high levels, 59% carbon reduction can be achieved during the future energy transition, even when the economy is still growing. In the past, electricity efficiency and conversion efficiency have significantly reduced carbon emissions. Future policymakers should pay more attention to passive systems to provide more final services, such as improved room insulation, streamlined vehicle designs, and smart energy management. • Proposed an extended Kaya identity framework for the systematic evaluation. • Evaluated the role of technical efficiency improvements at the most granular level. • 59% carbon reduction by improving technical efficiency during energy transition. • Passive systems highlighted for more carbon reduction potential. • Strongly promoting technical efficiency improvements in China's carbon neutrality. [ABSTRACT FROM AUTHOR]

The ambitious 2050 decarbonization strategy of the European Union (EU) must implement and reconcile several economic, social, and technological targets, such as continuous prosperity, growth, and increases in energy production and reductions in fossil fuel (FOS) intensity. These dimensions are included in a modified Kaya identity that, consequently, becomes a considerably useful accounting identity to analyze convergence among the EU member states toward a carbon neutrality target. Surprisingly, no comprehensive analysis of the convergence of the growth of all Kaya components toward the decarbonization target among the EU countries and the rest of the world has been conducted thus far. This study fills this gap in the literature by providing the first comprehensive evidence for the simultaneous convergence process of FOS reduction for all the Kaya components from 1990 to 2018. A balanced panel data is used in the study. The results reveal that there is evidence of σ -absolute and conditional β -convergence process for several groups of EU countries. Furthermore, to provide policy recommendations, we simulate alternative scenarios to assess when the target levels of decarbonization can be reached, providing relevant policy implications for the credibility of the 2050 EU policy regarding climate change. [ABSTRACT FROM AUTHOR]

This paper aims to analyze and compare the driving forces of the carbon dioxide emissions of the six highest emitters of the world, namely, China, the United States of America, the European Union, India, Russia, and Japan, which are responsible for more than the 67% of the emissions, during the period 1990–2018. The analysis is based on an enlarged Kaya-LMDI decomposition, considering five driving forces and a Granger causality study. Both techniques allow us to disentangle the relationship among the different driving forces and how they change from country to country. The main conclusion from the Kaya-LMDI analysis is that economic growth has been the main driving force that increases CO 2 emissions, and to a much lesser extent, the increase in population in most of the six analyzed economies. On the other hand, energy intensity is the main factor for reducing CO 2 emissions. Surprisingly enough, the end-use fuel-mix term seldom contributes to the decrease of the emissions, which proves that the use of renewable energy should still be actively promoted. It is worth highlighting the different behavior observed between the four developed countries and the two most populous developing ones, China and India. The Granger-causality analysis suggests that energy intensity Granger causes GDP in the developed countries, energy intensity also Granger causes CO 2 emissions in half of the countries and, GDP Granger causes CO 2 emissions only in one case, Japan. [Display omitted] • China and India should greatly improve their energy intensity and energy mix contributions to reduce their CO 2 emissions. • The USA and the EU shown promising trends concerning energy intensity and energy mix contributions capable of reducing their global CO 2 emissions. • Granger causality analysis suggests that energy intensity Granger causes most of the rest of driving forces. • Emission intensity of China, India and Russia is around four times greater than that of the USA, the EU and Japan. [ABSTRACT FROM AUTHOR]

• Evaluation of industrial eco-efficiency across Europe. • Scenarios affect the mitigation of emissions differently and the disposal of undesirable outputs becomes a costly activity. • Technological spillovers have led to progression toward the best technology. • Significant long run differences persist in terms of convergence. • Emissions per capita have been decreased in the 1995–2011 period in Europe. Eco-efficiency is the ability to create more goods and services with less impact on the environment while it consists a dominant strategy for the European Union in view of the New Green Deal. In recent years, its assessment, especially for the manufacturing sector, has attracted the interest of policymakers as a strategy in the pursuit of sustainability. However, a clear-cut direction on which industries should follow does not exist. In this applied study, we estimate distinct objectives of economic and ecological performance, introducing diverse scenarios regarding specific environmental pressures, by utilizing directional distance functions under a metafrontier framework. The methodology is implemented to a sample of 14 industries from the manufacturing sector from 27 European countries over the period 1995–2011. Our results reveal that the existence of a unified production technology set causes large differences in the eco-efficiency levels of the manufacturing sector while energy intensive industries can be characterized as the most eco-inefficient. Although the speed of industrial eco-efficiency convergence increases throughout the years, the case of CO 2 emissions presents an irregular behavior compared to the other greenhouse gases and air pollutants. Thus, a decomposition analysis of the manufacturing CO 2 emissions is considered as a further subject of interest in the study. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions from agriculture contribute to climate change. The consequences of unsustainable agricultural activity are polluted water, soil, air, and food. The agricultural sector has become one of the major contributors to global GHG emissions and is the world's second largest emitter after the energy sector, which includes emissions from power generation and transport. Latvian and Lithuanian agriculture generates about one fifth of GHG emissions, while Estonia generates only about one tenth of the country's GHG emissions. This paper investigates the GHG trends in agriculture from 1995 to 2019 and the driving forces of changes in GHG emissions from the agricultural sectors in the Baltic States (Lithuania, Latvia, and Estonia), which are helpful for formulating effective carbon reduction policies and strategies. The impact factors have on GHG emissions was analysed by using the Logarithmic Mean Divisia Index (LMDI) method based on Kaya identity. The aim of this study is to assess the dynamics of GHG emissions in agriculture and to identify the factors that have had the greatest impact on emissions. The analysis of the research data showed that in all three Baltic States GHG emissions from agriculture from 1995 to 2001–2002 decreased but later exceeded the level of 1995 (except for Lithuania). The analysis of the research data also revealed that the pollution caused by animal husbandry activities decreased. GHG intensity declined by 2–3% annually, but the structure of agriculture remained relatively stable. The decomposition of GHG emissions in agriculture showed very large temporary changes in the analysed factors and the agriculture of the Baltic States. GHG emissions are mainly increased by pollution due to the growing economy of the sector, and their decrease is mainly influenced by two factors—the decrease in the number of people employed in the agriculture sector and the decreasing intensity of GHGs in agriculture. The dependence of the result on the factors used for the decomposition analysis was investigated by the method of multivariate regression analysis. Regression analysis showed that the highest coefficient of determination (R2 = 0.93) was obtained for Estonian data and the lowest (R2 = 0.54) for Lithuanian data. In the case of Estonia, all factors were statistically significant; in the case of Latvia and Lithuania, one of the factors was statistically insignificant. The identified GHG emission factors allowed us to submit our insights for the reduction of emissions in the agriculture of the Baltic States. [ABSTRACT FROM AUTHOR]