Identification and analysis of driving factors of CO2 emissions from economic growth in Pakistan

Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China

Scientific measurement and analysis of the spatial and temporal distribution characteristics of agricultural carbon emissions (ACEs) and the influencing factors are important prerequisites for the formulation of reasonable ACEs reduction policies. Compared with previous studies, this paper fully considers the heterogeneity of rice carbon emission coefficients, measures and analyzes the spatial and temporal characteristics of ACEs in Jiangsu Province from three carbon sources, including agricultural land use, rice cultivation, and livestock and poultry breeding, and explores spatial clustering patterns and driving factors, which can provide a reference for agricultural low-carbon production. The results indicate that from 2005 to 2020, Jiangsu's ACEs showed a decreasing trend, with an average annual decrease of 0.32%, while agricultural carbon emission density (ACED) showed an increasing trend, with an average annual increase of 0.16%. The area with the highest values for ACEs is concentrated in the northern region of Jiangsu, while the areas with the highest values for ACED are distributed in the southern region. The spatial clustering characteristics of ACEs have been strengthening. The "H-H" agglomeration is mainly concentrated in Lianyungang and Suqian, while the "L-L" agglomeration is concentrated in Zhenjiang, Changzhou, and Wuxi. Each 1% change in rural population, economic development level, agricultural technology factors, agricultural industry structure, urbanization level, rural investment, and per capita disposable income of farmers causes changes of 0.112%, -0.127%, -0.116%, 0.192%, -0.110%, -0.114%, and -0.123% in Jiangsu's ACEs, respectively. To promote carbon emission reduction in agriculture in Jiangsu Province, we should actively promote the development of regional synergistic carbon reduction, accelerate the construction of new urbanization, and guide the coordinated development of agriculture, forestry, animal husbandry, and fisheries industries.

Does the construction of innovative cities promote urban green innovation?

As a typical example of an innovative governance environment, innovative city has become the focus of political and academic circles. Discussing the green innovation effect of this policy is beneficial in providing decision support for enhancing urban green innovation capability and solving the dilemma of urban resources. Based on data from 241 cities in China from 2005 to 2017, this paper takes innovative city construction as a "quasi-natural experiment" and uses the difference-in-differences method to empirically study the impact of China's innovative city construction on urban green innovation. The results show that innovative urban construction: has a positive effect on improving urban green innovation, and that this effect will gradually increase with time; promotes urban green innovation by expanding the scope of technology application, improving the efficiency of resource allocation and promoting the adjustment of industrial structure; and presents heterogeneity in cities' location, size and hierarchy. Finally, this paper proposes that the government should promote innovative policies in an orderly manner on the basis of following the principle of adapting measures to local conditions, as well as incorporating green innovation performance into the evaluation system for innovative city construction.

Changes in Energy-Related Carbon Dioxide Emissions of the Agricultural Sector in Poland from 2000 to 2019

This paper analyzes the changes in carbon dioxide (CO2) emissions related to energy consumption in the Polish agricultural sector between 2000 and 2019. Based on the Logarithmic Mean Divisia Index (LMDI), the changes in agricultural CO2 emissions are viewed in the context of changes in six factors, i.e., CO2 emission intensity, substitution of fossil fuels, penetration of renewable energies, energy intensity, labor productivity and number of employees. The analysis demonstrated that total energy consumption declined over the study period; this was related to a reduction in the intake of energy derived from solid fossil fuels (−1.05%), crude oil (−1.01%), electricity (−4.89%), and heat (−1.37%), and to an increased consumption of natural gas (5.78%) and biofuels (0.82%). Furthermore, it follows from the analysis that changes in CO2 emissions witnessed in that period were consistent with changes in energy consumption levels; this resulted from a negligible transformation of the energy mix (largely determined by fossil fuels). Generally, CO2 emissions declined over the study period at a rate comparable (−0.9%) to that of the reduction in energy consumption (−1.03%). In light of the LMDI method, the reduction in CO2 emissions from fuel consumption in the Polish agricultural sector was mainly driven by a reduction in energy intensity and in employment. Conversely, rapid growth in labor productivity was the key factor in increasing carbon dioxide emissions. Compared to these impacts, changes in other factors (i.e., emission intensity, energy mix and penetration of renewable energies) had an extremely small or marginal effect on the variation in CO2 emissions.

Carbon Emission Inversion Model from Provincial to Municipal Scale Based on Nighttime Light Remote Sensing and Improved STIRPAT

Carbon emissions and consequent climate change directly affect the sustainable development of ecological environment systems and human society, which is a pertinent issue of concern for all countries globally. The construction of a carbon emission inversion model has significant theoretical importance and practical significance for carbon emission accounting and control. Established carbon emission models usually adopt socio-economic parameters or energy statistics to calculate carbon emissions. However, high-precision estimates of carbon emissions in administrative regions lacking energy statistics are difficult. This problem is especially prominent in small-scale regions. Methods to accurately estimate carbon emissions in small-scale regions are needed. Based on nighttime light remote-sensing data and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, combined with the environmental Kuznets curve, this paper proposes an ISTIRPAT (Improved Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Through the improved STIRPAT model (ISTIRPAT) and panel data regression, provincial carbon emission inventory data were downscaled to the municipal level, and municipal scale carbon emission inventories were obtained. This study took the 17 cities and prefectures of Hubei Province, China, as an example to verify the accuracy of the model. Carbon emissions for 17 cities and prefectures from 2012 to 2018 calculated from the original STIRPAT model and the ISTIRPAT model were compared with real values. The results show that using the ISTIRPAT model to downscale the provincial carbon emission inventory to the municipal level, the inversion accuracy reached 0.9, which was higher than that of the original model. Overall, carbon emissions in Hubei Province showed an upward trend. Regarding the spatial distribution, the main carbon emission area was formed in the central part of Hubei Province as a ring-shaped mountain peak. The lowest carbon emissions in the central area expanded outward, increased, and gradually decreased to the edge of the province. The overall composition of carbon emissions in eastern Hubei was higher than those in western Hubei.

Determinants of smallholder farmers' choice on mulch film thickness in rural China

Agricultural mulch film pollution has become a prevailing concern. Studies have shown that the thickness of mulch film is a key factor affecting mulch film recycling, but research about farmers' choice on mulch film thickness is lacking. Based on survey data from 2025 households in five Chinese provinces in 2018, the Heckman two-stage model was used to analyze the influencing factors of farmers' choice on mulch film thicknesses. Mulch film had been used by 21.98% of the sample households, and 41.47% of the used mulch film did not meet the national thickness standard. The econometric results showed that farmers' product cognition and market factors were the two most important factors, and there was a significant negative correlation with the choice of film thickness. In addition, the choice of mulch film with different thicknesses was affected by household characteristics, subjective norms, and farmland property rights. Strengthening and stabilizing farmland property rights is a long-term mechanism to promote farmers to choose thicker mulch film. In addition to strengthening the production and sale of substandard film supervision, farmers' choice of film thickness should be included in village regulations and other rural grass-roots governance systems, especially in the mechanism design between agricultural farmland protective subsidies and the prevention of mulch film pollution, rather than just considering the recycling itself.

Driving forces and decoupling indicators for carbon emissions from the industrial sector in Egypt, Morocco, Algeria, and Tunisia

North Africa currently accounts for about 40% of Africa's total CO₂ emissions, and the industrial sector is one of the energy-intensive sectors in the region. To this end, special attention should be paid to this region if the African continent's GHG mitigation targets are to be achieved. An extended decomposition approach was combined with the Tapio method to explore the decoupling of CO₂ emissions from industrial growth in North African countries over the period of 1990-2016. The effects of five factors were assessed in the decoupling and the study took into account all fossil fuels used in the industrial sector of this region. Unlike Morocco, Egypt, Tunisia, and Algeria, this study did not consider Libya because of the unavailability of data. Meanwhile, the results showed that: (i) low decoupling was achieved in Tunisia, compared with Morocco and Egypt, where significant decoupling occurred significantly over the study period. (ii) Due to the slowdown in industrial growth, the decoupling analysis did not show satisfactory results in the case of Algeria. (iii) Scale effects contributed to promoting decoupling only in Algeria, while the energy intensity effect played a negative role in decoupling only in Tunisia. (iv) The energy structure effect played an important role in decoupling in Tunisia and Egypt, while the economic structural effect favored decoupling in Tunisia and Morocco alone. An energy policy conducive to the use of more renewable energy is needed to promote decoupling in North African countries.

How does industrial restructuring influence carbon emissions: City-level evidence from China

This study addresses the question of why industrial restructuring towards light industries or services sometimes fails to achieve carbon emission mitigation goals. By employing a new perspective of dividing industry segments into emission-dominating and non-emission-dominating ones based on Logarithmic Mean Divisia Index (LMDI) decomposition method, this paper analyses city-level carbon dioxide emission reduction performance under three distinctive industrial restructuring directions. Results indicate that carbon dioxide emission dominating segments are relatively fixed across cities, regardless of the various city types in China. The key point to achieve emission reduction through industrial restructuring is to identify and control the emission-dominating segments, instead of economic-leading ones. Besides, emission reduction performance of industrial restructuring from emission-dominating industry segments to services is better than that to non-emission-dominating ones. More importantly, industrial restructuring not involving output scale controlling of emission-dominating segments, or that recklessly rushing towards services are likely to fail the emission mitigation goal. This paper presents a strong international reference that merits cities facing policy hesitation over industrial restructuring directions while in pursuit of emission mitigation. It suggests that cities first focus on identifying the carbon dioxide dominating segments, of which the output scale should be controlled. For cities whose emission-dominating segments are not economic-leading ones, it is necessary to carry out industrial restructuring towards services or non-emission-dominating segments; while for cities heavily dependent on emission-dominating segments, energy efficiency should also be improved.

On the asymmetric effects of premature deindustrialization on CO2 emissions: evidence from Pakistan

In this modern era, environmental pollution is the biggest problem attached to industrialization. This study tries to ensure the relationship between industrialization and CO2 emissions in Pakistan for the time period 1980-2018 by using nonlinear ARDL model while controlling for urbanization, GDP, and human capital variables as a likely factor of CO2 emissions. Our foremost study objective is to examine whether or not the outcome of industrialization on CO2 emissions is symmetric or asymmetric for Pakistan that is one of the core suppliers to CO2 in South Asia, as the emissions were 0.82 million tons in 2018. Our result approves the presence of an asymmetric effect of industrialization shocks on CO2 emissions both in the short run and long run. The results reveal that industrialization increases emissions and deindustrialization decrease emissions, in short as well as long run, in Pakistan. Moreover, our finding also advises that urbanization and GDP variables have exerted a positive impact on CO2 emissions. Based on the findings, some policy suggestions are proposed for Pakistan.