Extended STIRPAT Model Research Articles

Impact of affluence, nuclear and alternative energy on US carbon emissions from 1960 to 2014

Using the extended STIRPAT model, this research examines the influence of various factors on US carbon emission from 1960 to 2019, including nuclear and alternative energy of total, fossil energy of total, GDP per capita, total population, urban population of total and merchandise trade of GDP. Ridge regression was used to perform the study. The research results show the significant of all factors on carbon emission. The estimated elastic coefficients reveal the most important factor influencing carbon emission is merchandise trade of GDP. GDP per capita has negative impact on carbon emission. Nuclear and alternative energy of total, urban population of total are also prominent influencing factors, while the other factors such as total population and fossil energy of total have less significant impact on carbon emission in US. These findings of this research will be of great significance for US to control its carbon emission in the future and to mitigate the global warming to some extent.

Impact of industrial agglomeration on haze pollution: New evidence from Bohai Sea Economic Region in China

To comply with recent sustainable development trends and initiatives for effectively controlling air pollution problems, this paper applies spatial econometrics and extended STIRPAT model to explore the impact of the industrial agglomeration (IA) on haze pollution (HP) using panel data from critical cities of Bohai Sea economic region (BSER) in China between 2006 and 2018. Influence path characteristics, regional heterogeneity effects and development trends of three major IA on the HP are systematically investigated. The results show that: (1) IA significantly correlated to HP in the BSER. The Siphon Effect in Beijing-Tianjin-Shijiazhuang region plays an important role in the HP and IA, and it weakened with the upgrading of industrial structure and industrial transfer. (2) Both IA and HP have significant spatial autocorrelation properties, the agglomeration and diffusion properties of IA and HP in specific regions are consistent along the time series. (3) However, systematic analysis of various industries indicated that the IA of the primary industry and the secondary industry has a significant positive impact on HP. Interestingly, IA of the third industry has a significant nonlinear effect on HP. Based on the results of the empirical analysis, this study provides insights on the spatial relationship between regional HP and industrial development. This paper gives an effective reference on promoting the distribution of resources and diversified green economic development paths.

A comprehensive insight into water pollution and driving forces in Western China—case study of Qinghai

Rapid industrialization and urbanization have caused many serious resources and environmental problems, including water scarcity and water pollution. Grey water footprint (GWF), estimating water pollution according to the volume of water required to assimilate pollutants, has caught large attention because of the requirement for meeting water scarcity and water pollution challenges. Qinghai, an arid or semi-arid region in Western China, is an important ecological barrier providing multiple ecological services with water as its key element. Recently it has experienced rapid urbanization which may cause serious water problems. Therefore, this study aims to investigate water pollution in Qinghai between 2000 and 2017 and to diagnose the related driving forces using the GWF model and the extended STIRPAT model. Results show that the total GWF increased between 2000 and 2017, from 1.28 E+10 to 1.97 E+10 m3. Agricultural GWF took the largest share with a mean proportion of 60%, followed by domestic GWF(8%) and industrial GWF(32%). Water environment load increased by 19.67%, while the GWF efficiency increased by 5.49 times. The GWF intensity decreased by 84.58%, reflecting water pollution level resulted in per unit of GDP decreased largely. Population and GDP per capita are the major drivers with contribution degrees of 70.20% and 39.65%, respectively. Industrial level had the least contribution, 5.34%, whereas energy intensity had a considerable negative impact, −36.66%, on GWF’s increase. In addition, policy suggestions are provided to mitigate GWF challenges of Qinghai. This study can provide scientific basis for decision-marking for sustainability.

Analyzing extended STIRPAT model of urbanization and CO2 emissions in Asian countries.

CO2 emissions tend to increase more rapidly in underdeveloped economies as compared to developed countries mainly in China, India, and Asia. One of the aspects that accounts for the increasing CO2 emissions is urbanization (UR) and it is increasing all over the world particularly in Asian and African regions. The present study examines the impact of energy use and UR on carbon emissions over the period 1995 to 2018 while using the extended STIRPAT model for Asian countries. Panel co-integration techniques and Granger causality test are applied on selected variables. FMOLS and DOLS methods are also applied to check for robustness. Findings confirm the presence of long-run co-integration among variables. The outcomes propose that energy consumption and UR have positive impact on CO2 emissions and output. Outcomes also reveal that financial development (FD) has negative effect on emissions of CO2 but positive effect on economic growth. Results of Granger causality technique indicate that long-run causality association exists among emissions of CO2, economic growth, and UR. In the short run (SR), bidirectional causal relationship has been found between trade openness and FD.

Antibiotic resistance genes in sediments of the Yangtze Estuary: From 2007 to 2019

To better understand the occurrence and succession of antibiotic resistance genes (ARGs) in the environment, the investigation of ARGs in sediment for a long time scale is urgently needed. In this study, sediment samples were taken in the Yangtze Estuarine area from 2007 to 2019, and the interannual variations in ARGs and their possible physicochemical and socioeconomic influencing factors were analyzed. The results showed that the abundance of ARGs, including sul1, sul2, tetM, tetW, aac(6′)-Ib and qnrS, was higher in recent years (from 2015 to 2019) than that in earlier years (from 2007 to 2011), and heavier ARG pollution was found in Wusongkou (WSK) samples than in Liuhekou (LHK) samples. According to the redundancy discriminant analysis (RDA) and correlation analysis, the antibiotics (especially individual antibiotic categories, including oxytetracycline, doxycycline hyclate and norfloxacin), metals and a metal resistance gene (zntA) and total organic carbon (TOC) showed significant correlations to ARGs. In addition, antibiotics, metals, TOC and ARGs were also significantly correlated with several socioeconomic indices. Furthermore, the extended STIRPAT model analysis revealed that the second industry product and the first industry product were the major socioeconomic driver factors for the ARG distribution at WSK and LHK, respectively. Overall, with socioeconomic development, antibiotics, metals, TOC and ARGs increased in sediment. In addition, antibiotics, metals and TOC may participate in the regulation of the occurrence and distribution of ARGs in the Yangtze Estuary for the long time scale.

Driving factors analysis of agricultural carbon emissions based on extended STIRPAT model of Jiangsu Province, China

AbstractSTIRPAT (stochastic impact by regression on population, affluence, and technology) model is used to identify the influencing factors of agricultural carbon emissions in Jiangsu province. By referring to Kaya identity and combining with the actual situation of agricultural carbon emissions, four basic influencing factors were obtained: agricultural production efficiency, agricultural structure, agricultural economic development level, and agricultural population size. In addition, urbanization, mechanization, and natural disaster level were listed as influencing factors. The results demonstrated: (a) Urbanization was the first promoting factor of agricultural carbon emissions, indicating a 0.2510% increase in agricultural carbon emissions due to a 1% increase in urbanization. The other three positive factors were, respectively, agricultural mechanization, agricultural structure, and agricultural economic development and their influence indexes were 0.1481, 0.1163, and 0.0845, respectively. (b) Agricultural production efficiency was the most important factor to restrain agricultural carbon emissions. For every 1% increase in agricultural production efficiency, corresponding agricultural carbon emissions would be reduced by 0.3288%. Agricultural population size was also an important factor to reduce agricultural carbon emissions and its influence index was −0.045. Finally, we propose policy recommendations including implementation of orderly urbanization, dependence and development of low carbon technology, establishment of agricultural carbon compensation mechanism, etc.

Uncovering trajectories and impact factors of CO2 emissions: A sectoral and spatially disaggregated revisit in Beijing

To clarify trajectories and impact factors for CO2 emissions is critical to emissions reduction, especially at finer sectoral and spatial resolutions. We statistically disaggregated city-level sectoral CO2 emissions to districts of Beijing during 2004–2013 and examined the impact factors using an extended STIRPAT model. Results revealed that CO2 emissions decreased in manufacturing sector, while increased in residential and transportation sectors, which were promising fields for emissions reduction. Spatial dynamics of district-level CO2 emissions indicated that industrial carbon reduction should focus on not only core urban area but also the new merged hotspot districts. The impact estimations indicating by elastic coefficients revealed that population size (0.6, 0.49, 1.06), GDP per capita (0.63, 0.56, 0.8), energy intensity (0.93, 0.86, 0.96) and secondary industrialization level (0.36, 0.8, 0.88) can lead to increase in total, industrial and transportation CO2 emissions, while urban land area size (−0.47, −0.24, −0.81) exerted negative effects on them. In addition, private vehicle ownership (0.45) positively affect transportation CO2 emissions. Residential CO2 emissions were positively influenced by urban land area size (0.44), urbanization level (0.18), population density (0.49) and private vehicle ownership (0.11). Given these findings, policy recommendations for CO2 emissions reduction and mitigation were suggested for low-carbon urban development.

How do age structure and urban form influence household CO2 emissions in road transport? Evidence from municipalities in Norway in 2009, 2011 and 2013

This article investigates the influence of age structure on CO2 emissions from household road transport by using an extended STIRPAT model plus data from 380 Norwegian municipalities for 2009, 2011 and 2013. After controlling for population, household income, age structures, household size, and different urban forms (urbanization, urban density, housing type, building density), the paper reveals that the age group responsible for the highest CO2 emissions is 50–69, followed by 20–34 and 35–49. Moreover, compared with other groups, the road transport activities of age group 35–49 are highly constrained by household income. The paper also shows that there is an inverted U-shape relationship between household CO2 road-related emissions and building densities. However, it indicates certain limitations on city planners when it comes to reducing household CO2 road-related emissions by bringing the downtown area closer. Moreover, the paper also identifies a so-called compensatory mechanism supporting the hypothesis that building densities have positive effects. Furthermore, the coefficient of low-density housing is positive and significant, implying that the private gardens of low-density housing might not be the reason for the hypothesized compensatory mechanism. However, this remains a question worth investigating.

Multi-drivers and multi-mechanism analysis for city-level energy consumption in Suzhou based on the extended STIRPAT model

Examining the driving factors of energy consumption at city scale, is an important measure for effectively achieving energy saving and emission reduction. An extended STIRPAT model based on the classical IPAT identity is adopted to uncover the main drivers for total energy consumption in Suzhou, an important manufacturing city in China, during 2006 to 2016. The main conclusions are as follow: (1) Foreign trade, economic growth, and industrialization are the three dominant contributors to energy consumption increments. Furthermore, quite significant portion of Suzhou’s foreign trade is processing exports. The export products are mainly high-energy consumption such as electromechanical, chemical, textile, etc. The export-oriented economy is accelerating the economic growth and industrialization, and also strengthening the energy intensive regional development mode. (2) Urbanization and population size are also the main driving factors for the rapid growth of energy consumption in Suzhou. The population scale effect exhibits the urban function of absorb employment and agglomerate population in Suzhou, it also demonstrates the growing energy demands caused by population growth and the accompanying urbanization process. (3) Energy consumption intensity and energy consumption structure are the important influencing factor in curbing energy consumption in Suzhou. Energy saving effects played by energy consumption intensity are stronger than energy consumption structure. The coal-based energy consumption structure has not been changed fundamentally during the research period. Therefore, the energy saving effect of energy structure has not been fully utilized. The current and future major strategies for controlling energy consumption in Suzhou should further improve energy utilization efficiency, optimize energy consumption structure, and reduce the proportion of coal consumption.

Peaking Industrial Energy-Related CO2 Emissions in Typical Transformation Region: Paths and Mechanism

Reducing CO2 emissions of industrial energy consumption plays a significant role in achieving the goal of CO2 emissions peak and decreasing total CO2 emissions in northeast China. This study proposed an extended STIRPAT model to predict CO2 emissions peak of industrial energy consumption in Jilin Province under the four scenarios (baseline scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS), and low-carbon scenario (LCS)). We analyze the influences of various factors on the peak time and values of CO2 emissions and explore the reduction path and mechanism to achieve CO2 emissions peak in industrial energy consumption. The results show that the peak time of the four scenarios is respectively 2026, 2030, 2035 and 2043, and the peak values are separately 147.87 million tons, 16.94 million tons, 190.89 million tons and 22.973 million tons. Due to conforming to the general disciplines of industrial development, the result in ELS is selected as the optimal scenario. The impact degrees of various factors on the peak value are listed as industrial CO2 emissions efficiency of energy consumption > industrialized rate > GDP > urbanization rate > industrial energy intensity > the share of renewable energy consumption. But not all factors affect the peak time. Only two factors including industrial clean-coal and low-carbon technology and industrialized rate do effect on the peak time. Clean coal technology, low carbon technology and industrial restructuring have become inevitable choices to peak ahead of time. However, developing clean coal and low-carbon technologies, adjusting the industrial structure, promoting the upgrading of the industrial structure and reducing the growth rate of industrialization can effectively reduce the peak value. Then, the pathway and mechanism to reducing industrial carbon emissions were proposed under different scenarios. The approach and the pathway and mechanism are expected to offer better decision support to targeted carbon emission peak in northeast of China.

Examining the impact factors of the water environment using the extended STIRPAT model: A Case Study in Sichuan.

China's rapid social and economic development has led to a significant deterioration in the water environment, which has limited sustainable regional development. Therefore, understanding the specific factors that affect the water environment is vital for future water conservation efforts. From a social economy perspective, this paper used population, the economy, urbanization, technological level, water consumption, and other factors to expand the STIRPAT model, after which partial least squares was applied to solve the model parameters and comprehensively analyze the impact of regional development on the water environment in Sichuan Province from 2007 to 2017. It was found that the main factors affecting the water environment were resident population, urbanization, service industry development, and industrialization, with the industrialization factor being found to have a reverse waste-sewage water discharge inhibition. In addition, it was found that during the study period, there was no environmental Kuznets curve between water resource environmental pollution and economic growth in Sichuan Province. Finally, some policy recommendations for improving the water environment were given based on the results.

Does urbanization boost environmental impacts in Colombia? An extended STIRPAT–LCA approach

The relationship between urbanization and technological progress on environment, mainly CO2 emissions, has been extensively studied in the last decades; however, little effort has been made to understand such a relationships in developing countries. This work focuses on understanding the effects of urbanization and some technological factors on the Colombian electricity consumption. This topic is addressed from several environmental impact dimensions rather than solely focusing on CO2 emissions. An extended STIRPAT model, in combination with LCA approaches, was applied in a balanced panel dataset of 27 states, over the period of 2003 to 2018. Our findings suggest that urbanization is the major driver of electricity consumption and climate change in Colombia. Our results address the lack of information about this topic in developing countries and also, they contribute to understand the main features of emissions in Colombia and the key driving forces behind their environmental impacts. Finally and given the current trends on population and urban growth, we conclude with some recommendations aimed to relevant stakeholders.

Dilution effect of the building area on energy intensity in urban residential buildings

Urban residential buildings make large contributions to energy consumption. Energy consumption per square meter is most widely used to measure energy efficiency in urban residential buildings. This study aims to explore whether it is an appropriate indicator. An extended STIRPAT model was used based on the survey data from 867 households. Here we present that building area per household has a dilution effect on energy consumption per square meter. Neglecting this dilution effect leads to a significant overestimation of the effectiveness of building energy savings standards. Further analysis suggests that the peak of energy consumption per square meter in China’s urban residential buildings occurred in 2012 when accounting for the dilution effect, which is 11 years later than it would have occurred without considering the dilution effect. Overall, overlooking the dilution effect may lead to misleading judgments of crucial energy-saving policy tools, as well as the ongoing trend of residential energy consumption in China.

Impact of Influencing Factors on CO2 Emissions in the Yangtze River Delta during Urbanization

The Yangtze River Delta (YRD) is China’s largest urban agglomeration with a rapid urbanization process. This paper analyzes the dynamic relationship between urbanization rate, energy intensity, GDP per capita, and population with CO2 emissions in YRD over 1990–2011 based on the extended STIRPAT model, impulse response function, and variance decomposition. A support vector machine model was constructed to further predict the scenarios of YRD’s CO2 emissions from 2015–2020. The results show that YRD’s CO2 emissions continuously increased during the sample period and are predicted to increase over 2015–2020. Energy intensity is the most influential factor, both in the short and long term, and the total population contributes the least. However, the influencing magnitude of energy intensity tends to decrease in the long term. The increase of urbanization rate is still accompanied by the increase of CO2 emissions in YRD, but an inverted-U shape relationship between them may exist in the long term. The contribution of GDP per capita to CO2 emissions is higher than the population and urbanization rate, and its contribution rate for CO2 emissions is growing. The Kuznets curve does not exist in the current YRD.

Impact of affluence and fossil energy on China carbon emissions using STIRPAT model.

Using the extended STIRPAT model, this research examines the influence of various factors on China carbon emission from 1971 to 2014, including total nuclear and alternative energy, total fossil energy, GDP per capita, total population, total urban population, merchandise trade of GDP, and services value added of GDP. Ridge regression was employed to perform the study. The research results show the positivity and significance of all factors on carbon emission. The estimated elastic coefficients reveal the most important factor influencing carbon emission is GDP per capita. Total fossil energy, total urban population, and nuclear energy of total energy use are also prominent influencing factors, while other factors such as value-added services of GDP and merchandise trade of GDP have less significant impacts on carbon emission in China. These findings of the research will be of great significance for China to control its carbon emission in the future and to mitigate global warming to some extent.

The role of financial development in the process of climate change: Evidence from different panel models in China

This paper selects the annual panel data of China and its provinces from 1997 to 2015 and explores the impacts of financial development on CO2 emissions based on the extended STIRPAT model. In order to analyze the impacts of financial development on CO2 emissions under different levels of financial development, full-sample panels and sub-panels were estimated. The estimation results show that financial development efficiency and stock trading volume have positive impacts on CO2 emissions, while financial development scale and the market value of listed companies have negative impacts on CO2 emissions. The elasticity coefficients of the financial development variables are different for different sub-panels with different financial development. In addition, we do robustness tests by estimating the panel data model of four different sub-panels, which estimation results are consistent with the previous regression results. Finally, we suggest China's policy makers the following policy implications: China's government should formulate and improve financial credit policies, which can encourage financial institutions to carry out green credit business. Laws and regulations of stock markets should match the environmental protection and carbon emission reduction. China's government should formulate and implement fiscal and taxation policies to encourage enterprises to prevent high energy consumption and carbon emission, especially non-stated owned enterprises and small size enterprises. Moreover, China should gradually develop the domestic carbon finance market and carbon trading market, which can promote carbon emission reduction and doesn't require additional subsidy from the government.

Investigation of key contributors of CO2 emissions in extended STIRPAT model for newly industrialized countries: A dynamic common correlated estimator (DCCE) approach

Abstract The study examined the impact of different forces on carbon dioxide emissions for ten newly industrialized countries (NIC’s) by applying the extended STIRPAT model for the period 1991–2013. Moreover, study utilizes the regression of group mean dynamic common correlated estimator (DCCE) to analyze the relationship between carbon dioxide emissions (CO2), population, affluence, technology and extended factors such as labor productivity, urban employment level, population carrying intensity of real economy, energy mix, trade openness and energy intensity. The results from DCCE approach are compared with fully modified ordinary least square and dynamic ordinary least square techniques for robustness. Results of the study suggest that population, GDP per capita and CO 2 emission intensity along with energy intensity are main contributors for CO2 emissions for NIC’s, while population carrying capacity of real economy have no significant impact on CO2 emission level. Furthermore, energy mix and trade openness have marginal contribution in CO2 emissions. The policymakers can use these results to develop appropriate policies for economic growth through industrial development by curtailing the level of CO 2 emissions.

Examining the effects of socioeconomic development on China's carbon productivity: A panel data analysis

China, which is the largest carbon emitter and the largest developing country in the world, faces the challenge of achieving energy conservation and emission reduction without sacrificing economic development. Improving carbon productivity consists a possible way to seek a coordination between economic development and carbon emission reduction. Therefore, it is of great significance to examine the effects of socioeconomic development on China's carbon productivity and accordingly provide policy suggestions for China's low-carbon economic development. However, this topic has not been adequately addressed in previous studies. In order to fill this gap, this study detailed an empirical investigation into the impacts of socioeconomic development on China's carbon productivity. First, aided by spatial analysis methods, a detailed analysis of the spatiotemporal patterns and dynamics of China's province-level carbon productivity was conducted. Moreover, using an extended STIRPAT model and panel data modeling technique, the effects of a range of socioeconomic factors on China's carbon productivity were quantitatively examined. The results indicated that China's carbon productivity increased gradually between 1997 and 2016, and carbon productivity in East China was much higher than that of their counterparts in Central China and West China. Provincial administrative units with highly developed economies witnessed spectacular increases in carbon productivity. Panel data analysis demonstrated that GDP per capita, technology level, trade openness, and foreign direct investment exerted positive effects, while energy consumption structure, industrial proportion, and urbanization level exerted negative effects, on China's carbon productivity. Based on the findings of this study, a series of policy suggestions with respect to improving China's carbon productivity were proposed.

CO2 emissions from China’s power industry: Policy implications from both macro and micro perspectives

China has become the global largest CO2 emitter, and CO2 emissions from China’s power industry account for almost half of the total. Therefore, the power sector plays a key role in achieving low-carbon economy. It is imperative to formulate comprehensive policy implications for the power sector from both macro and micro perspectives to achieve the target of emission reduction. This paper selected seven socio-economic and technological factors involved in the whole power industry to investigate the mechanism driving CO2 emissions based on the extended STIRPAT model. The results show that the order of factors’ absolute elasticity coefficients is GDP per capita (0.245) > power generation efficiency (−0.237) > power consuming efficiency (−0.189) > line loss rate (−0.184) > electricity practitioner population (0.143) > industrial structure (0.082) > generation structure (0.068). Further, it is necessary to discuss how a specific power enterprise responds under the low-carbon generation policy to maximize its own interest. The evolutionary game model between power enterprise and government is established and results indicate that it is critical to slash low-carbon production cost, enhance returns on low-carbon production and strengthen the regulation of power enterprises, and reduce the supervision cost of government. Accordingly, policy recommendations are proposed from both macro and micro perspectives for CO2 emission reduction of power industry.

Beijing-Tianjin-Hebei scenario design and model research under the constraint of emission peak

Beijing-Tianjin-Hebei cooperation and taking the lead in the peak of carbon emissions is an important research direction of Beijing-Tianjin-Hebei collaborative development. According to the social development and coordination degree of Beijing-Tianjin-Hebei, three scenarios of low, medium and high modes were set up, as well as a variety of combinations, based on the extended STIRPAT model, predicting carbon emissions peak, and choose the best development mode. On this basis, to explore factors impacts on peak value and peak time. The results show that: The optimal development pattern and peak time of Beijing-Tianjin-Hebei under peak-emission constraints are similar. Beijing should develop high-low mode in the future under carbon peak constraint, Tianjin and hebei choose high-low + industrial optimization model. The peak times of Beijing, Tianjin and Hebei are 2025; Population is the main influencing factor to promote the peak time and the peak-value decrease of Beijing-Tianjin-Hebei, while the rapid economic development is the main influencing factor of peak time delay and increasing peak value; Therefore, the coordinated development of Beijing-Tianjin-Hebei future on the basis of guarantee the quality of economic development, it should be the main control of the total urban population, rational control of economic growth, optimize the adjustment of industrial structure.