Search Results (16)

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

China’s transportation sector plays a significant role in reducing carbon dioxide (CO₂) and air pollution. Previous studies have predominantly utilized scenario analysis to forecast emissions for the next 30 to 50 years based on coefficients from a base year. To elucidate the current state of gas emissions in the transportation sector, this study employed panel data for 10 types of gas emissions from 2001 to 2020, analyzing their emission characteristics, tendencies, and synergistic effects. Utilizing the Kaya equation and the logarithmic mean division index (LMDI) decomposition method, we developed a model of pollutant emissions that considers the synergistic effects, pollution emission intensity, energy mix, energy consumption intensity, and population. The results show that all pollutants in the transportation sector decreased except for NH₃ and CO₂. There was a synergistic effect between air pollutants and CO₂ emissions, but the reduction was not significant. From 2013 to 2020, the transportation sector shifted from a high emission intensity with low synergy to a low emission intensity with high synergy. The results indicate that off-road mobile vehicles, on-road diesel vehicles, and motorcycles became the main source of emissions from transportation in certain provinces, and a key area requiring attention in policy development. Gasoline consumption was identified as the primary contributor to the significant increase in synergistic emission variability in the transportation sector. These results provide policymakers with practical ways to optimize emission reduction pathways. Full article

Electricity consumption is closely linked to economic growth, social development, and carbon emissions. In order to fill the gap of previous studies on the decomposition of electricity consumption drivers that have not adequately considered carbon emission constraint, this study constructs the Kaya extended model of electricity consumption and analyzes the effects of drivers in industrial and residential sectors using the Logarithmic Mean Divisia Index (LMDI) method, and empirically explores the temporal and spatial differences in electricity consumption. Results show that: (1) During 2005–2021, the total final electricity consumption growth in Guangdong was much higher than that in Yunnan, but the average annual growth rate in Guangdong was lower, and the largest growth in both provinces was in the industrial sector. (2) The labor productivity level effect is the primary driver that increases total final electricity consumption (Guangdong: 78.5%, Yunnan: 87.1%), and the industrial carbon emission intensity effect is the primary driver that decreases total final electricity consumption (Guangdong: −75.3%, Yunnan: −72.3%). (3) The year-to-year effect of each driver by subsector is overall positively correlated with the year-to-year change in the corresponding driver, and declining carbon emission intensity is a major factor in reducing electricity consumption. (4) The difference in each effect between Guangdong and Yunnan is mainly determined by a change in the corresponding driver and subsectoral electricity consumption. Policy implications are put forward to promote energy conservation and the realization of the carbon neutrality goal. 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

With the continuous development of the social economy, carbon emissions from various buildings are increasing. As the most important category of building carbon emissions, the rapid peaking of public buildings is an important part of achieving carbon peak and carbon neutrality. This paper is based on the industrial background of the energy consumption structure of Henan Province, a central province in the developing country of China. Firstly, the energy consumption intensity of buildings and public buildings in Henan Province from 2010 to 2020 was calculated according to the energy balance sheet. The Kaya–LMDI decomposition method was also used to analyse the carbon emissions of public buildings, determining the impact of each influencing parameter on public buildings. Secondly, the scenario prediction model Monte Carlo was run 100,000 times to set the stochastic parameters of the variables in the model to predict the time of carbon peak and carbon emissions. The analysis results indicated that: ① Carbon emissions in Henan Province have exhibited a steady growth trend, increasing from 1533 t in 2010 to 6561 t in 2020; ② The primary factors influencing carbon emissions of public buildings in Henan Province were urbanization rate, public floor area per capita, and energy intensity per unit of public floor area; and ③ Carbon emissions of public buildings in Henan Province followed an inverted U-shaped distribution and are expected to peak at approximately 7423 t by the year 2035. The research method in this paper can guide the simulation study of peak carbon emission prediction in Henan Province based on the influencing parameters of carbon emission from different types of buildings. Moreover, the results of this paper can provide a reference for a more precise study of building carbon reduction in similar regions of developing countries. Full article

The improvement in energy efficiency (EE) and increasing consumption of renewable energy sources (RES) in manufacturing play an important role in pursuing sustainable development in the Baltic States and contribute to the transition to a low-carbon economy. This paper presents the results of a detailed analysis of the channel through which EE, along with structural activity changes, passes energy intensity and total energy savings and in combination with other key drivers results in reductions in greenhouse gas (GHG) emissions in manufacturing in Estonia, Latvia, and Lithuania during the period 2010–2020, taking into account the role of transformations in the energy and climate framework of the European Union (EU). The Fisher Ideal Index, the Kaya identity, the Logarithmic Mean Divisia Index (LMDI), and comparative analysis methods are used. The results of the impact analysis of key drivers on energy intensity showed different contributions towards improvements in EE and structural activity changes to changes in energy intensity in manufacturing, which decreased by 53.1% in Estonia, by 30.5% in Lithuania, and by 16.5% in Latvia. The dominant role of EE improvements on total energy savings is identified. The results of the GHG decomposition analysis showed that because of improvements in energy intensity, reductions in the share of fossil fuels, and increases in labour productivity, number of employees, and emissions intensity, the GHG emissions decreased by 35.5% in Estonia, 40.4% in Latvia, and 8.1% in Lithuania. The results confirm the need for new policies and the implementation of relevant commitments to save energy and increase the contribution of RES in all three countries. Full article

Traffic carbon emissions have a non-negligible impact on global climate change. Effective estimation and control of carbon emissions from tourism transport will contribute to the reduction in the amount of global carbon emissions. Based on the panel data of Dunhuang in western China from 2010 to 2019, the process analysis method was used to estimate the carbon emissions from tourism traffic of Dunhuang. By establishing the Kaya identity of tourism traffic carbon emissions, the LMDI decomposition method was used to reveal the contribution of different factors to the change in tourism traffic carbon emissions. The results showed that the impact of tourism traffic carbon emissions was diversified; we found three main factors of promoting carbon emissions, namely the number of tourists, tourism expenditure per capita, and energy consumption per unit of passenger turnover. However, the contribution of tourism activities to GDP, passenger turnover per unit of GDP, and energy structure largely inhibited the increase in carbon emissions. Full article

Rational water use is the basis for sustainable development. The issue of how to use limited water resources to satisfy the high rate of economic development has attracted a great deal of attention from society. This paper presents a quantitative analysis of the intrinsic relationship between economic growth and industrial water use changes in Hubei Province based on panel data from 2004 to 2019. With the help of the Tapio decoupling model, the problem of decoupling the economic growth of Hubei Province and the water use of the three industries in 15 years was discussed. On the basis of Kaya’s extended identity, the Logarithmic Mean Divisia Index (LMDI) index decomposition method is used to evaluate the driving factors and steady state changes in the three industries’ water use. The results show that, with regard to the decoupling state, there are three decoupling states between economic growth and industrial water use in Hubei province: negative decoupling, strong decoupling, and weak decoupling, which showed a phase characteristic. From the decomposition of the factors, the industrial structure effect and the water intensity effect are the key factors that determine the decoupling of economic growth and industrial water use in Hubei Province, as well as the core driving force to promote the decoupling state. According to the development trend, Hubei Province needs to take into account the efficiency and affordability of water resources in the process of promoting social and economic development. Therefore, in line with the research outcomes, this study provides effective and feasible recommendations for promoting sustainable economic and social development in Hubei Province. Full article

For the Yangtze River Delta (YRD) region of China, exploring the spatio-temporal characteristics of carbon emissions from energy consumption (CEECs) and their influencing factors is crucial to achieving carbon peaking and carbon neutrality as soon as possible. In this study, an improved LMDI decomposition model based on the Tapio model and Kaya’s equation was proposed. Combined with the improved LMDI and k-means cluster analysis methods, the energy structure, energy intensity, unit industrial output value and population size were selected as the driving factors, and the contribution of each driving factor to the CEECs of prefecture-level cities was quantitatively analyzed. Our study found that: (1) By 2020, the total amount of CEECs in the 26 prefecture-level cities in the YRD will stabilize, while their intensity has shown a downward trend in recent years. (2) The decoupling relationship between CEECs and economic development generally showed a trend from negative decoupling to decoupling. The dominant factor in decoupling was generally the shift of DEL values towards urbanization rate and energy intensity and the open utilization of energy technologies. (3) From 2000 to 2010, the dominant factors affecting CEECs in 26 cities were energy intensity and energy structure, followed by industrial output value and urbanization rate. In general, the promotion effect of economic development on carbon emissions in the YRD region was greater than the inhibitory effect. After 2010, the restrictive effect of various factors on CEECs increased significantly, among which the role of gross industrial output was crucial. The research results can provide a scientific policy basis for the subsequent spatial management and control of carbon emission reduction and carbon neutrality in the YRD region at a finer scale. Full article

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. Full article

In-depth analysis of the decoupling state between cultivated land carbon emissions and cultivated land use factors can provide a basis for coordinating the relationship between food security and ecological environment. On the base of systematically calculating the carbon source of cultivated land, this paper calculated the carbon emission of cultivated land in China’s main agricultural production areas from 2000 to 2020, and explored its temporal and spatial pattern and evolution process. Then, using the LMDI decomposition method and the improved kaya identity, the factors affecting the carbon emissions of cultivated land are divided into five effects: structure, economy, technology, society and population, and then the Tapio decoupling theory is used to analyze the relationship between carbon emissions and these five effects. At the same time, to explore the further relationship between carbon emissions and cultivated land structure, we also studied the decoupling state between carbon emissions and the cultivated land area of 6 main crops. The results showed: during the study period, carbon emissions experienced three stages: fluctuating growth, accelerated growth and slow decline. In the most recent stage, structural, economic and population effects still have some impact on the carbon emissions of cultivated land, changes in cultivated land area where cotton, sugar and tobacco are planted will still affect its carbon emissions. To intervene, policy measures such as promoting the use of clean energy, increasing agricultural imports, and increasing carbon taxes for some industries can be considered. Full article

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. Full article

The deployment of renewable energy sources (RES) is an essential strategic objective of sustainable energy development in Estonia, Latvia and Lithuania. Their growing contribution to the total primary energy supply can significantly facilitate the transition to a low-carbon economy. The paper provides findings from an in-depth comparative analysis of RES deployment trends during 2010–2019 in the Baltic countries in the context of energy policy documents of the European Union (EU). The dynamics of targeted RES indicators according to the Renewables Directive 2009/28/EC and National Programmes and the role of renewable energies in mitigating climate change are analysed. A key role of the heating and cooling sector in deploying RES is highlighted and a necessity to implement radical changes in the transport sector of the Baltic countries is revealed. The paper examines changes in energy-related greenhouse gas (GHG) emissions and the impact of driving factors in Estonia, Latvia, Lithuania and other countries of the Baltic Sea Region (BSR). The Kaya identity and the logarithmic mean Divisia index (LMDI) method are used for the decomposition analysis. Based on the analysis conducted, the impact of population change, economic growth, decline of energy intensity, RES deployment and reduction of emission intensity on change of GHG emissions in countries of the BSR and, on average, in the EU-27 during 2010–2019 is revealed. Full article

Urbanization is considered a main indicator of regional economic development due to its positive effect on promoting industrial development; however, many regions, especially developing countries, have troubled in its negative effect—the aggravating environmental pollution. Many researchers have addressed that the rapid urbanization stimulated the expansion of the industrial production and increased the industrial pollutant emissions. However, this statement is exposed to a grave drawback in that urbanization not only expands industrial production but also improves labor productivity and changes industrial structure. To make up this drawback, we first decompose the influence of urbanization impacts on the industrial pollutant emissions into the scale effect, the intensive effect, and the structure effect by using the Kaya Identity and the LMDI Method; second, we perform an empirical study of the three effects by applying the spatial panel model on the basis of the data from 282 prefecture-level cities of China from 2003 to 2014. Our results indicate that (1) there are significant reverse U-shapes between China’s urbanization rate and the volume of industrial wastewater discharge, sulfur dioxide emissions and soot (dust) emissions; (2) the relationship between China’s urbanization and the industrial pollutant emissions depends on the scale effect, the intensive effect and the structure effect jointly. Specifically, the scale effect and the structure effect tend to aggravate the industrial wastewater discharge, the sulfur dioxide emissions and the soot (dust) emissions in China’s cities, while the intensive effect results in decreasing the three types of industrial pollutant emissions; (3) there are significant spatial autocorrelations of the industrial pollutant emissions among China’s cities, but the spatial spillover effect is non-existent or non-significant. We attempt to explain this contradiction due to the fact that the vast rural areas around China’s cities serve as sponge belts and absorb the spatial spillover of the industrial pollutant emissions from cities. According to the results, we argue the decomposition of the three effects is necessary and meaningful, it establishes a cornerstone in understanding the definite relationship between urbanization and industrial pollutant emissions, and effectively contributes to the relative policy making. Full article