Search Results (53)

Understanding the historical peak situation and the rules for CO₂ emissions from buildings helps to formulate reasonable building mitigation strategies, accelerating the achievement of the Chinese government’s carbon peak goal. As developed regions, cities in the Guangdong–Hong Kong–Macao Great Bay Area (GBA) provide valuable reference cases. This study quantified the historical building CO₂ emissions of GBA cities and analyzed the contribution of driving factors using the Kaya identity and logarithmic mean Divisia index. Furthermore, we assessed the historical peak situation using the MK trend test method and discussed the reasons behind the inter-city difference in the peak situation shown by the environmental Kuznets curve. The results indicate that the building-related CO₂ emissions of the GBA will slowly increase to 96.90 Mt CO₂ by 2020 and that P&C buildings accounted for a larger proportion of emissions. Emission factors and population made the largest positive and negative contributions, respectively, to this total. At the city level, Guangzhou, Shenzhen, and Hong Kong ranked as the top three sources of building CO₂ emissions. Hong Kong peaked, Dongguan and Macao plateaued, and other cities maintained either slow or quick growth. CO₂ emissions unit area, per capita building CO₂ emissions, and building CO₂ emissions reached a peak in that order. This study provides a valuable reference for formulating a city-level path showing building CO₂ emissions peaks. Full article

The issue of improving the effectiveness of international climate policy, one of the main goals of which is to reduce greenhouse gas (GHG) emissions, poses a critical and acute challenge for the global economic system. At every COP conference and in every IPCC report, it is evident that current measures fall short. To address this gap, this study examines the structure and trends of global climate policy development through content analysis, PRISMA methodology, and correlation and regression analysis using censored Bayesian Tobit regression. The obtained results are supplemented with the LMDI (Logarithmic Mean Divisia Index) decomposition of the Kaya identity. The research covers 198 countries and 4241 documents spanning 1950 to 2023 that shape global climate policy. The results showed that (1) the success of climate goals varies depending on policy instruments, institutional conditions, and the time frame of analysis; (2) the greatest success in achieving climate targets was often observed in countries that adopted moderate, realistic, and institutionally supported targets; (3) in some cases, an overachievement of targets and GHG emissions reduction was a temporal observation or the result of economic decline; (4) in countries without officially declared targets, emissions also continued under similar growth trends, calling into question the effectiveness of current methods of setting up CO₂ emissions reduction targets. These findings provide a deeper understanding of the factors determining the effectiveness of climate policy. They highlight key barriers to achieving too ambitious emission reduction targets, which can lead to economic shocks and a subsequent increase in environmental impact. Ultimately, this research can contribute to the development of more realistic and effective decarbonization strategies. Full article

In an era of growing climate concerns and complex environmental policy challenges, novel approaches for accurate carbon emissions measurement are urgently needed. This article introduces an innovative approach for predicting carbon dioxide emissions by analyzing the interaction between energy consumption and production efficiency, measured through an entropy-to-marginal product ratio. Unlike conventional metrics such as Eurostat measurements or the Kaya identity, our framework establishes explicit connections to fundamental physical laws governing energy transformation while offering flexible elasticity parameters that capture non-linear relationships between efficiency improvements and emission reductions. The research combines theoretical modeling with empirical validation across ten European countries, demonstrating how the entropy-based methodology accounts for both production complexity and energy efficiency where traditional linear models fall short. Analysis reveals that energy-efficient countries demonstrate lower entropy maximization under stable conditions, indicating a direct relationship between operational efficiency and environmental impact. Although the model demonstrates strong predictive capabilities with an exceptional accuracy/information cost ratio, limitations exist in achieving accuracy in some country cases. This study concludes by evaluating these strengths and constraints, acknowledging the need for extended time series analysis and sector-specific applications, and providing clear directions for future research that bridge this promising theoretical contribution with practical environmental policy applications. Full article

This research seeks to elucidate the relationship between economic activities, energy consumption, and CO₂ emissions, thereby contributing to a deeper understanding of the economic dimensions of climate change mitigation efforts within the European context, which may be useful for developing policies to mitigate CO₂ emissions and promote sustainable development. This study investigates world CO₂ emissions and their relation to population growth and finds a strong positive relation based on data from 1969 to 2023. The World Kaya Identity has been applied to understand how changes in the involved factors affect CO₂ emissions over time. When studying the more complex relation between the variables by controlling for energy use, GDP, and carbon intensity based on the Kaya Identity, the authors identified an overall long-term coupling of all factors. Considering short-term variations, population growth appears to have an insignificant effect, and carbon intensity appears most influential on CO₂ emissions. As a next step, we take a disaggregated view on different country settings, economic sectors, and energy sources to further analyze the role carbon intensity plays for increased CO₂ emissions. Here, we lay a special focus on the European perspective. This descriptive analysis lets us draw some general conclusions regarding strategies for reducing the negative impact of CO₂ emissions and political efforts for sustainability transformations. This study is important for the current state of science, since a clear economic assessment of the negative effects of carbon dioxide is necessary for planning measures and costs in the ecological sphere, the correct assessment of the impact on the health of the population, the prospective implementation of preventive measures at all levels, and financing measures to reduce the negative effects of carbon dioxide. The authors found a significant positive effect of GDPpc, energy intensity, and carbon intensity on impact and an insignificant effect on the population. Thus, an unexpected increase in the population likely does not have short-term effects on CO₂ emissions, and the responses to GDPpc and energy intensity both decrease after some periods, while the shock in carbon intensity shows a significant effect even after 10 years. This is reasonable in the sense that both increases in GDP and energy intensity might be alleviated by technological progress and, thus, only show a short-term positive effect on CO₂ emissions. The carbon intensity of energy consumption is more crucial for the long-term change of CO₂ emissions. For this reason, we study the decomposition of energy use in more detail by considering descriptive statistics over time and over different sectors and countries. Full article

The airline industry is currently navigating a pivotal period characterized by rapid development and increasing global pressure to reduce carbon emissions. Airlines, as the first to be significantly impacted, must actively manage their carbon footprints, adopt carbon abatement technologies, and address the inherent risks in this transformation. This paper examines the risk factors correlated with the technology transformation of carbon abatement and proposes effective abatement strategies. Using panel data of China Southern Airlines from 2009 to 2023 and applying the Logarithmic Mean Divisia Index (LMDI) method based on the Kaya identity, we analyze the differential impacts of various factors on unit carbon emissions. Multiple scenarios, derived from the influences of these factors, are constructed, and the Monte Carlo algorithm is employed to simulate the impact and volatility of correlated risks in the technology transformation for the abatement of carbon emissions. The findings are as follows: on the one hand, carbon emissions are strongly driven by energy consumption (0.99), flight volume (0.941), flight hours (0.931), transportation turnover (0.923), and take-off frequency (0.833). On the other hand, technology (56%) and scale (54.74%) significantly reduce unit carbon emissions, while take-off frequency negatively impacts emissions (−35.19%). Technology-related risks are controllable and relatively stable, whereas scale-related risks are highly uncertain. Additionally, operation-related risks can be partially hedged to ensure a certain level of risk controllability. Full article

The improvement of energy efficiency (EE) and growing consumption of renewable energy sources (RES) in the commercial and public services sector are playing important roles in seeking to pursue sustainable development in the Baltic States and contributing to the transition to a low-carbon economy. This paper provides findings from a detailed analysis of energy intensity trends in economic sectors from 2005 to 2022 in three countries, considering the role of transformations in the energy and climate framework of the European Union (EU). Based on the Fisher Ideal Index application, the different contributions from improving EE and structural changes are revealed. The dominant role of EE improvements in energy savings is identified in Estonia and Lithuania, and structural changes are dominant in Latvia. Changes in energy-related greenhouse gas (GHG) emissions in the commercial and public services sector and the main determinants of their reduction are examined. Based on applying the Kaya identity and the logarithmic mean Divisia index (LMDI) method, decreasing energy intensity is the most important determinant in all three countries. Due to the different extents of RES deployment, their role was very important in Estonia and Latia but was less effective in Lithuania. Reduction in emission intensity has the largest impact in Latvia. The GHG emissions decreased by 34.1% in Estonia, 17.5% in Latvia, and 16.7% in Lithuania. The results confirm the need for new policies, implementation of relevant EE measures, and the growing contribution from RES in Latvia and Lithuania. Full article

Carbon Pricing Instruments (CPIs), such as Carbon Taxes and Emission Trading Schemes (ETSs), have been launched in several countries, primarily in Europe and North America, as a means of limiting the emissions of greenhouse gases (GHGs) which have been known to cause climate change. The adoption of these measures in Asia has been controversial, with many arguing that they would limit economic development in the region. We review the CPIs of 18 Asian economies, 7 of which have adopted a CPI during our review period from 1990 to 2021. We perform a comparative analysis of the economies in Asia, applying the Kaya Identity to decompose the variables affecting carbon emissions and the Nearest Neighbor Matching technique to compare the effect that CPIs have on countries adopting these policies relative to other jurisdictions. We found a positive and significant effect of CPIs on reducing carbon emissions in the Asian countries compared in our study. This offers crucial insights for policymakers, stressing the effectiveness of CPIs in balancing environmental sustainability with economic development in the region. Full article

This study uses factor decomposition analysis from the extended framework of Kaya’s identity to determine the factors influencing CO₂ emissions in 138 countries worldwide from 2019–2022. The results revealed that CO₂ emissions decreased due to economic stagnation caused by COVID-19 from 2019–2020. From 2020–2021, CO₂ emissions increased due to economic recovery and increased dependence on fossil energy. However, the timing of recovery varies with income level. In low-income countries, economic recovery progressed more from 2021–2022 than from 2020–2021, which was the opposite of the situation in developed countries. In terms of technological factors, carbon intensity was a factor in increasing emissions in high-income countries during the recovery period, whereas fossil dependency and energy efficiency were factors in increasing emissions in upper-medium- and lower-medium-income countries, respectively. The results of the decoupling analysis indicate that lower-medium-income countries tend to exhibit undesirable decoupling. Furthermore, from an analysis of the transition of decoupling states, it is necessary to reduce the carbon intensity and fossil fuel dependency to achieve a desirable decoupling relationship between CO₂ and economic development. Full article

Recently, an improvement of the United Nations Human Development Index ($HDI$), named the Thermodynamic Human Development Index ($THDI$), has been introduced to link socio-economics to environmental and technical pillars of sustainable development. In this paper, the $THDI$ is linked to the Kaya identity to bring out the quantities useful in energy economics and to obtain a clearer tool for the evaluation of sustainability. Moreover, the $THDI$ has been normalized for use as an index for the analysis of sustainability. The component related to environmental emissions, which is included in the $THDI$, can be linked to the Kaya identity. This linkage allows us to use the $THDI$ for the analysis of scenarios, which is useful for evaluating the possible impacts of any future actions on the development of countries. Full article

The low-carbon development of the transportation sector is crucial for China to achieve its national goals of carbon peaking and carbon neutrality. Since China is a vast country with unbalanced regional development, there are considerable differences in the levels of carbon dioxide emissions from the transportation sector across regions. Therefore, revealing the influencing factors that shape the characteristics of transportation carbon dioxide emissions (TCO₂) can inform tailored sub-national carbon reduction strategies based on local conditions, which is an important technical approach for achieving national goals. Based on an extended Kaya identity, we derived indicators of the impacts on provincial TCO₂ from factors such as economic development, population density, energy structure, transportation efficiency, technology research and development (R&D), infrastructure construction, transportation operation conditions, and residents’ transportation behavior. Using a multi-indicator joint characterization method, we explored the characteristics of provincial TCO₂ in China in 2019. By applying Ward’s method to hierarchical clustering, the thirty provinces of China were classified into six characteristic types (Types I to VI). Based on the total TCO₂ (TC), the intensity of TCO₂ (TI), and the per capita TCO₂ (TP) calculated for each province in 2019, the priority control directions and indicators for carbon reduction in each type were obtained through relative relationships with provincial averages and correlation analysis with the indicators. Specifically, Type I and Type IV can be categorized as TP-controlled, Type II and Type III as TC-controlled, and Type V and Type VI as TI-controlled. Finally, we provided typological strategies and key performance indicators (KPIs) relevant to local governments to better achieve carbon reduction goals in each provincial type. It can promote cooperative development and collaborative governance in carbon reduction across regions and the unified implementation of China’s dual-carbon goals. Full article

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. Full article

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

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

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. Full article

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