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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]

In order to quantify the contribution of the mitigation strategies, an extended Kaya identity has been proposed in this paper for decomposing the various factors that influence the CO2 emission. To this end, we provided a detailed decomposition of the carbon intensity and energy intensity, which enables the quantification of clean energy development and electrification. The logarithmic mean divisia index (LMDI) has been applied to the historical data to quantify the contributions of the various factors affecting the CO2 emissions. Further, the global energy interconnection (GEI) scenario has been introduced for providing a systematic solution to meet the 2°C goal of the Paris Agreement. By combining LMDI with the scenario analysis, the mitigation potential of the various factors for CO2 emission has been analyzed. Results from the historical data indicate that economic development and population growth contribute the most to the increase in CO2 emissions, whereas improvement in the power generation efficiency predominantly helps in emission reduction. A numerical analysis, performed for obtaining the projected future carbon emissions, suggests that clean energy development and electrification are the top two factors that can decrease CO2 emissions, thus showing their great potential for mitigation in the future. Moreover, the carbon capture and storage technology serves as an important supplementary mitigation method.

The global economy is on the verge of one of the greatest transitions in modern history. The ability to ensure sustainable economic development and prosperity while significantly reducing consumption of energy resources and generated greenhouse gas emissions is a global challenge that affects every country in the world. To assess whether economies are ready for this challenge, there is an urgent need to examine this dual relationship between economic growth and climate change measures. European Green Deal strategy has set the ambitious goal of Europe becoming the first climate-neutral continent by 2050, boosting competitiveness and long-term prosperity of the economy. Kaya identity and LMDI decomposition is applied to examine how European Union countries have been coping with these countereffects historically. The decomposition analysis is conducted for the EU-28 (including the UK) countries for a 10-year study period from 2010 to 2019. This study analyses the main drivers of changes in GHG emissions in European Union and estimates the progress made in implementing the Green Deal targets. The results show that in the EU, energy efficiency improvements have twice the effect on reducing GHG emission compared to RES strategies. The effect of economic growth was the main offsetting factor hindering the achievement of larger GHG emission reductions. More in-depth ex-ante and ex-post investigation is performed for the Baltic States. A novel forecasting technique is applied to project GHG emissions under three different development scenarios, such as the scenario with existing measures, the scenario with additional measures, and the business-as-usual scenario. The results show that the current climate policies in the Baltic States are not sufficient to achieve the 2030 emission reduction targets and that greater efforts should be made to enforce climate mitigation measures in the economies.

In order to explore the change law and driving factors of industrial water use in Guangzhou,this paper divides the influencing factors of industrial water use into economic development level,population size,water use efficiency and industrial structure by the Kaya identity and Logarithmic Mean Divisia Index (LMDI),and analyzes the effects of various factors on the changes in industrial water use in Guangzhou from 2000 to 2015.The results show that:The economic development effect and population size effect are the primary and secondary driving factors for the increase on the industrial water use in Guangzhou,with cumulative contribution values of 8.538 billion m3 and 4.583 billion m3,respectively;The water use efficiency effect and industrial structure effect are the primary and secondary inhibiting factors for the increase on the industrial water use in Guangzhou,with cumulative contribution values of -11.789 billion m3 and -2.547 billion m3,respectively.

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.

CO2 emissions from the burning of fossil fuels are the primary cause of global warming. Much attention has been focused on the CO2 directly emitted by each country, but relatively little attention has been paid to the amount of emissions associated with the consumption of goods and services in each country. Consumption-based accounting of CO2 emissions differs from traditional, production-based inventories because of imports and exports of goods and services that, either directly or indirectly, involve CO2 emissions. Here, using the latest available data, we present a global consumption-based CO2 emissions inventory and calculations of associated consumption-based energy and carbon intensities. We find that, in 2004, 23% of global CO2emissions, or 6.2 gigatonnes CO2, were traded internationally, primarily as exports from China and other emerging markets to consumers in developed countries. In some wealthy countries, including Switzerland, Sweden, Austria, the United Kingdom, and France, >30% of consumption-based emissions were imported, with net imports to many Europeans of >4 tons CO2 per person in 2004. Net import of emissions to the United States in the same year was somewhat less: 10.8% of total consumption-based emissions and 2.4 tons CO2 per person. In contrast, 22.5% of the emissions produced in China in 2004 were exported, on net, to consumers elsewhere. Consumption-based accounting of CO2 emissions demonstrates the potential for international carbon leakage. Sharing responsibility for emissions among producers and consumers could facilitate international agreement on global climate policy that is now hindered by concerns over the regional and historical inequity of emissions.

Vietnam has witnessed a rapid increase in national-level CO2 emissions due to rising urbanization, economic expansion, export growth, and industrial development. Moreover, to support the ambitious economic growth targets, reliance on and consumption of fossil fuels are increasing by each passing year. With this circumstance, this study aims to analyze the key drivers of CO2 emissions in Vietnam from 1990 to 2016 using the Kaya identity and decomposition method. Following this approach, CO2 emissions have been decomposed into five effect categories comprising population, affluence, energy intensity, fuel mix, and emission intensity. As per the results, CO2 emissions in Vietnam were mainly driven by rising affluence (58.5%) and changing fuel mix (33.2%) which have resulted from improved living standards, rapid industrial development, and higher fossil fuel consumption. Moreover, population (13.8%) and emission intensity (3.1%) exhibited a relatively lower impact on CO2 emissions during 1990–2016. However, energy intensity (− 8.7%) was the only negative driver which has resulted in the slowdown of carbon emissions in Vietnam. Based on the analysis of energy policy development, the share of renewable energy resources was still quite low in the national energy mix with higher reliance on traditional fossil fuels (mainly coal and petroleum). Therefore, to make a transition towards low-carbon economic growth, significant improvements in energy efficiency and emission intensity are necessary together with national energy mix restructuring for low-carbon economic growth. [ABSTRACT FROM AUTHOR]

The G7 and Brazil, Russia, India, China and South Africa (BRICS) nations have committed to greenhouse gas emission reduction according to targets set out in the Paris Agreement and Copenhagen Accord. The objective of this paper is to develop an index decomposition framework and apply it for comparison of the drivers behind carbon dioxide emission. The impact of economic and technological development on greenhouse gas emissions is assessed by applying the Kaya identity. The index decomposition analysis is carried out by applying the Shapley value. In this paper, we seek to compare the two groups of countries—G7 and BRICS—in regard to the underlying trends in the energy‐related carbon dioxide emission during 1990–2015. The comparison of these two groups for countries draws some light on the pathways of decarbonization of the economies in developing and developed countries. The time period covered spans over the sub‐periods of economic growth and decline. Energy intensity appeared as the major factor pushing the carbon dioxide emissions down in the developed countries (i.e., G7 group). The latter effect was much lower for the developing countries (i.e., BRICS group) and was offset by the affluence effect. Further development of the renewable energy is important for the developing countries in order to start exploiting the carbon factor effect. [ABSTRACT FROM AUTHOR]

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.)

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To effectively combat global warming, an enormous reduction in CO2 emissions is required. Cameroon, which is currently the largest emitter of CO2 in the CEMAC subregion, has committed to reducing its greenhouse gas emissions by 32% by 2035. However, previous studies in Cameroon have only addressed the relationship between economic growth, energy consumption, and CO2 emissions without estimating all causal relationships at the same time. Moreover, no study has yet decomposed this country's CO2 emissions to date. To fill these research gaps and further assess the determinants of these CO2 emissions, an extended Kaya identity and the Logarithm Mean Divisia Index (LMDI I) have been applied in this paper to identify, quantify, and explain the main drivers of Cameroon's CO2 emissions from 2007 to 2014. Seven effects were measured and the main findings show that carbon intensity and the emission factor increased by 0.57% and 107.50% respectively. Regarding contributions to the increase of CO2 emissions, the population effect was the most positive followed by the activity effect, whereas the energy intensity, the substitution of fossil fuels and the penetration of renewable energies have contributed to reduce the CO2 emission. To enable Cameroon to not only achieve the goals of its vision but also develop a low-carbon economy, this paper provides some proposed avenues that should be considered by policymakers. [ABSTRACT FROM AUTHOR]

Abstract The Greenhouse gas emissions one of the hot topics all around the world which are causing climate change. In the face of anthropogenic climate change, increasing the carbon dioxide emissions as well as the development of Turkey seems to be a serious challenge for the success of global carbon dioxide emission reduction efforts. A decomposition analysis of historical and projected carbon dioxide emissions from fossil fuel combustion of Turkey has been investigated by using the Logarithmic Mean Divisia Index technique by considering in particular carbon intensity, energy mix, energy intensity, affluence and population effects. The projected data have been obtained from International Panel on Climate Change based sustainable emission scenario. The results show that the all effects on carbon dioxide emissions were positive for historical evaluation. On the other hand, carbon intensity, energy mix and energy intensity effects on carbon dioxide emissions were negative while affluence and population effects were still positive for projected evaluations. Therefore, decision makers should be reconsider the carbon dioxide emissions reduction targets and some related policies. Highlights • Past and future CO 2 emissions decomposition analysis of Turkey has been investigated. • Turkey should be act according to B1 scenario to decrease CO 2 emissions. • Affluence and population effects were the main factors affecting CO 2 emissions change. • Energy mix and energy intensity effects on CO 2 emissions were negative. • The share of renewable energy in Turkey's energy use should be further increased. [ABSTRACT FROM AUTHOR]

Abstract The present research proposes an analysis on the inequality of the consumption of electricity, different typologies of primary energy, namely natural gas, coal and oil, and carbon emissions in the period 2008–2016 within European Union. A decomposition in within and between country groups on the basis of their GDP per capita is also developed, in order to identify the main contributions to the inequality. Furthermore, carbon emissions are also decomposed according to the Kaya identity with the aim to assess which are the main sources of inequality. The analysis shows that the principal source of inequality is represented by the differences in GDP, especially for the energy consumption; whereas carbon emissions evidence a stable level of inequality during the period of analysis. Highlights • Energy and carbon emissions inequality in EU28 decomposed in between and within contributions. • GDP represents the main source of inequality for energy consumption. • Carbon emissions highlight a stable trend in the period of analysis. [ABSTRACT FROM AUTHOR]

Highlights • Urban scaling studies lack factors contributing to emission (in-) efficiency. • We merge urban scaling with Kaya Identity leading to urban kaya relation. • We propose an alternative regression method to analyse complex scaling relations. • Affluence and technology play a crucial role in determining emission efficiency. Abstract Urban areas play an unprecedented role in potentially mitigating climate change and supporting sustainable development. In light of the rapid urbanisation in many parts on the globe, it is crucial to understand the relationship between settlement size and CO 2 emission efficiency of cities. Recent literature on urban scaling properties of emissions as a function of population size has led to contradictory results and more importantly, lacked an in-depth investigation of the essential factors and causes explaining such scaling properties. Therefore, in analogy to the well-established Kaya Identity, we develop a relation combining the involved exponents. We demonstrate that application of this Urban Kaya Relation will enable a comprehensive understanding about the intrinsic factors determining emission efficiencies in large cities by applying it to a global dataset of 61 cities. Contrary to traditional urban scaling studies which use Ordinary Least Squares (OLS) regression, we show that the Reduced Major Axis (RMA) is necessary when complex relations among scaling exponents are to be investigated. RMA is given by the geometric mean of the two OLS slopes obtained by interchanging the dependent and independent variable. We discuss the potential of the Urban Kaya Relation in mainstreaming local actions for climate change mitigation. [ABSTRACT FROM AUTHOR]

Abstract Decoupling economic development from carbon emissions generated from China's commercial buildings (CECCB) is regarded as an important indicator for evaluating the energy efficiency of the commercial building sector in China. Our study is the first to propose a decoupling method based on a Logarithmic Mean Divisia Index (LMDI) decomposition analysis with the Kaya identity to analyze the relationship between economic development in China's Tertiary Industry and the CECCB growth at both national and municipal levels. The following three main results are found. (1) At the national level, commercial building sector decoupling from 2001 to 2015 is limited. Only four decoupling stages are observed at the municipal level with the ordering of municipal decoupling measured as follows: Tianjin > Beijing > Shanghai > Chongqing (2001−2010), Chongqing > Beijing > Tianjin > Shanghai (2011–2015). (2) Two extended versions of Environmental Kuznets Curves further show what drives different decoupling levels in the four municipalities. (3) More significant decoupling effects observed in recent years can be attributed to significant improvements made in the energy efficiency work of China's commercial building sector. Overall, our approach successfully covers a research gap relevant to the decoupling of the relationship between CECCB growth and the economic development of China's Tertiary Industry. Furthermore, we believe our results can be used to guide the evaluation of energy efficiency work in China's commercial building sector, and such efforts can also enrich diverse research conducted on China's low carbon economic systems. Graphical abstract Decoupling elasticity values in China's commercial building sector at both national and municipal levels for 2000–2015. Unlabelled Image Highlights • A decoupling analysis using Kaya and Logarithmic Mean Divisia Index methods is proposed. • The ordering of decoupling levels for 2001–2010 is Tianjin > Beijing > Shanghai > Chongqing. • The ordering of decoupling levels for 2011–2015 is Chongqing > Beijing > Tianjin > Shanghai. • More significant decoupling effects can be achieved by enhancing levels of energy efficiency. [ABSTRACT FROM AUTHOR]

International efforts to avoid dangerous climate change aim for global carbon dioxide (CO _2 ) emissions to be net-zero by midcentury. Such a goal will require both drastically reducing emissions from high-income countries and avoiding large increases in emissions from still-developing countries. Yet most analyses focus on rich-country emissions reductions, with much less attention to trends in low-income countries. Here, we use a Kaya framework to analyze patterns and trends in CO _2 emissions from the combustion of fossil fuels in Africa between 1990 and 2017. In total, African CO _2 emissions were just 4% of global fossil fuel emissions in 2017, or 1185 MtCO _2 , having grown by 4.6% yr ^−1 on average over the period 1990–2017 (cf the global growth rate of 2.2% yr ^−1 over the same period). In 2017, 10 countries accounted for about 87% of the continent’s emissions. Despite modest recent reductions in some countries’ CO _2 emissions, projections of rapid growth of population and per capita GDP will drive future increases in emissions. Indeed, if the continent-wide average growth rate of 2010–2017 persists, by 2030 Africa’s emissions will have risen by ∼30% (to 1545 MtCO _2 ). Moreover, if increases in carbon intensity also continue, Africa’s emissions would be substantially higher. In either case, such growth is at odds with international climate goals. Achieving such goals will require that the energy for African countries’ development instead come from non-emitting sources.

The Kaya identity is a powerful index displaying the influence of individual carbon dioxide (CO2) sources on CO2 emissions. The sources are disaggregated into representative factors such as population, gross domestic product (GDP) per capita, energy intensity of the GDP, and carbon footprint of energy. However, the Kaya identity has limitations as it is merely an accounting equation and does not allow for an examination of the hidden causalities among the factors. Analyzing the causal relationships between the individual Kaya identity factors and their respective subcomponents is necessary to identify the real and relevant drivers of CO2 emissions. In this study we evaluated these causal relationships by conducting a parallel multiple mediation analysis, whereby we used the fossil fuel CO2 flux based on the Open-Source Data Inventory of Anthropogenic CO2 emissions (ODIAC). We found out that the indirect effects from the decomposed variables on the CO2 flux are significant. However, the Kaya identity factors show neither strong nor even significant mediating effects. This demonstrates that the influence individual Kaya identity factors have on CO2 directly emitted to the atmosphere is not primarily due to changes in their input factors, namely the decomposed variables.