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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]

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.

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]

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.

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]

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.

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.)

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]

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.

Knowledge about the driving forces behind greenhouse gasses (GHG) emissions is crucial for informed and evidence-based policy towards mitigation of GHG emission and changing production and consumption patterns. Both national and regional-level authorities are capable of addressing their actions more effectively if they have information about the spatial distribution of phenomena related to the policies they conduct. In this context, the main aim of this paper is to explain the regional differences in carbon intensity in Poland. The differences in carbon intensity between regions and the national average were analysed using index decomposition analysis (IDA). Aggregate carbon intensity for regional economies as well as the carbon intensity of households was investigated. For both levels of analysis: total emissions and emission from households economic development is the key factor responsible for the inter-regional differences in carbon emission per capita. In the case of total emissions, the second important factor influencing these differences is the structure of the national power system, i.e., its concentration and the production of energy from fossil fuels. For households, disposable income per capita is a key factor of differences in CO2 emission per capita between regions. Higher households’ incomes contribute to higher emission per capita, mostly due to the shift in consumption towards more energy- and material-intensive goods. The contribution of energy emissivity is quite low and not as varied as in the case of income. This suggests that policy instruments targeted at the consumption of fuels can be rather uniform across regions, while more developed regions should also be subject to measures supporting less energy-intensive consumption. On the other hand, policy in less developed regions should prevent them from following the path of per capita emissions growth.

The industrial sector dominates the global energy consumption and carbon emissions in end use sectors, and it faces challenges in emission reductions to reach the Paris Agreement goals. This paper analyzes and quantifies the relationship between industrialization, energy systems, and carbon emissions. Firstly, it forecasts the global and regional industrialization trends under Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway2 (SSP2) scenarios. Then, it projects the global and regional energy consumption that aligns with the industrialization trend, and optimizes the global energy supply system using the Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE) model for the industrial sector. Moreover, it develops an expanded Kaya identity to comprehensively investigate the drivers of industrial carbon emissions. In addition, it employs a Logarithmic Mean Divisia Index (LMDI) approach to track the historical contributions of various drivers of carbon emissions, as well as predictions into the future. This paper finds that economic development and population growth are the two largest drivers for historical industrial CO2 emissions, and that carbon intensity and industry energy intensity are the top two drivers for the decrease of future industrial CO2 emissions. Finally, it proposes three modes, i.e., clean supply, electrification, and energy efficiency for industrial emission reduction.

Recent empirical studies often support the positive relationship between corporate environmental performance (CEP) in terms of carbon dioxide (CO2) and greenhouse gas (GHG) emissions and corporate financial performance (CFP). However, this depends on the measurements of CEP (the absolute and relative CEP) and CFP (accounting‐based and market‐based CFP). To understand the relationship structurally, based on the literature, this study proposes identity models that integrate CO2 and GHG emissions and financial factors. The models decompose CO2 (GHG) emissions into carbon intensity (GHG intensity), energy intensity, the cost‐to‐sales ratio, the total‐assets‐turnover ratio (TATR), leverage, and equity. The model of supply‐chain GHG emissions additionally adopts supply‐chain GHG intensity. As a decomposition method, this study uses the log‐mean Divisia index. As an application example of the CO2 model, this study targets Japanese manufacturing firms in 16 sectors from fiscal years (FY) 2011 to 2015. Results show that the change in CO2 emissions as of 2015 (−802.1 kilotonnes [kt]) is decomposed into 2922.5 kt for carbon intensity, −26036.3 kt for energy intensity, −6350.5 kt for the cost‐to‐sales ratio, −8495.6 kt for the TATR, −7912.3 kt for leverage, and 45070.1 kt for equity. Average values of relative contribution ratios are 20.6% for carbon intensity, 19.1% for energy intensity, and the remaining approximately 60% for financial factors. Among the 16 sectors, as of 2015, the change in total CO2 emission is statistically significantly positive for equity and significantly negative for the TATR and leverage. [ABSTRACT FROM AUTHOR]

Developing low carbon city is a global strategy for achieving carbon emission reduction. However, the evolution process of becoming a low carbon city remains unexplored, which is not conductive to the promotion of low carbon city. This study examines the evolution of low carbon city from process characteristic perspective. The evolution processes are analyzed by establishing the relationship between city's economic development and carbon emission performance. By adopting Kaya Identity method, city's emission characteristics in the process of promoting low carbon city are decomposed into energy structure, energy intensity, economic output, industrial structure and population. The performances of these five characteristics in different evolution processes are analyzed. By using the data collected from case cities of Singapore, Beijing, and New York, the evolution process and the corresponding emission characteristics of these cities have been investigated. The key findings from this study are: (1) a city successively goes through three turning points (TP) and four processes (P-Ⅰ, P-Ⅱ, P-Ⅲ, P-Ⅳ) to shift from carbon intensive to low carbon. (2) Performances of the five emission characteristics for cities vary significantly between the four evolution processes. The findings of this study help city governments understand the process they position in and the gap between their emission performances and their goals of becoming a low carbon city. This understanding allows the decision-makers to take proper emission reduction measures which shall incorporate city's emission characteristics in the corresponding process. [ABSTRACT FROM AUTHOR]

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]