Stochastic Impacts By Regression On Population, Affluence, And Technology Research Articles

Decomposition of China’s Carbon Emissions Responsibility from the Perspective of Technological Heterogeneity

A global agreement has been reached on the reduction in greenhouse gas emissions. Worldwide, countries have implemented measures to tackle carbon emission issues by establishing aggregate targets and decomposing responsibilities. This study aims to decompose carbon emissions by creating an input–output model that incorporates multivariate factors like energy consumption and water consumption, together with a ZSG-DEA (zero-sum data envelopment analysis) model considering technological heterogeneity (Tech-ZSG-DEA). Based on the total carbon emission data predicted using the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the carbon emission efficiency of 30 provinces in China is evaluated according to multiple principles. This is achieved by considering variations in population, economy, energy consumption, and water consumption across different locations. The efficiency findings indicate a discrepancy between the initial allocation and the highest efficiency value of 1. The traditional ZSG-DEA model overlooks regional disparities and may worsen carbon emission pressures in less developed areas. In contrast, the Tech-ZSG-DEA model, which considers regional technological diversity, allows more efficient regions to help alleviate some of the carbon emission burden and considers economic and social benefits. There is a large difference in the emission responsibility of the provinces based on the different decomposition principles. Finally, relevant policy recommendations are provided, such as the formulation of differentiated and inclusively coordinated emission plans. In addition, there are also mechanisms for coordinating interests and joint prevention among different regions.

An Empirical Investigation of GDP, Industrialization, Population, Renewable Energy and CO2 Emission in Bangladesh: Bridging EKC-STIRPAT Models

This research paper explores the presence of the Environmental Kuznets Curve (EKC) in Bangladesh, delving into the intricate relationships among GDP, industrialization, renewable energy utilization, and CO2 emissions. Utilizing the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, our investigation spans the 1992 to 2021 timeframe, offering a comprehensive analysis of the interplay between these crucial factors in the context of Bangladesh's environmental sustainability. In this study, the F-bound test is utilized to establish the cointegration relationship among variables. And the short-run and long-run elasticity of explanatory variables is investigated by employing the Autoregressive Distributed Lag (ARDL) approach. This research also employed a pairwise granger causality test to explore the direction of causality between them. Empirical results prove the existence of a cointegration relationship among CO2 emission, GDP per capita, a quadratic form of GDP per capita, renewable energy, industrialization, and population. The findings provide strong support for the presence of the EKC in the case of Bangladesh. Besides that, significant inverse relationship was also obtained between CO2 emission and renewable energy consumption. Additionally, the study found the detrimental long-term effects of industrialization on the environment. The EKC hypothesis argues that Bangladesh can accomplish sustainable economic development. To attain the goal country should adopt appropriate government policies and ensure their implication. This study strongly advocates using sustainable energy sources and implementing regulations on pollutant industries.

The impact of urbanization and economic growth on carbon dioxide emission in sub-Saharan African countries: a perspective from the spatial-temporal approach.

Sub-Saharan Africa (SSA) is seeing exceptional urbanization and economic expansion rates. Therefore, the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) parameters and the spatial econometric framework are used in this work to examine the influence of economic growth and urbanization on SSA's CO2 emissions. Likewise, to determine the spatial effect and understand how factors influence the spatial dependence of carbon emissions, the study builds a spatial Durbin model (SDM). In line with the findings, the spatial correlation test revealed the spatial correlations across various countries. This indicates that the changes in sub-Saharan African country's CO2 emissions impacted nearby countries and the countries themselves. Additionally, the findings reveal that, in the SSA's countries, urbanization, economic growth, industrial structure, trade, and population, excluding energy intensity, which failed the significant test, all positively influence CO2 outflows, in line with the spatial econometric model's findings. Thus, energy intensity shares an adverse impact on carbon emissions. As an outcome, energy intensity reduces carbon dioxide emissions in nearby nations and the entire region. Thus, the study recommends that policymakers account for the effects of spatial spillover when establishing low-carbon policies, encouraging a low-carbon lifestyle, promoting environmentally friendly technologies, and improving regional collaboration.

Historical development, impact mechanism and future trends of nitrogen footprint in Wuxi City, China

Human activities have changed the biogeochemical cycle of nitrogen, leading to a large amount of reactive nitrogen (Nr) into the environment, aggravating a series of environmental problems, affecting human and ecosystem health. Cities are the core areas driving nitrogen cycling in terrestrial ecosystems, however, there are numerous influencing factors and their contributions are unclear. The nitrogen footprint is an important index to understand the impact of human activities on the environment, however, the calculation of urban nitrogen footprint needs a simplified and accurate system method. Here we use a nitrogen footprint calculation model at the urban system level based on system nitrogen balance, and a multi-factor extended STIRPAT (stochastic impact by regression on population, affluence, and technology) model suitable for analyzing the impact mechanism of nitrogen footprint to estimate nitrogen footprint of Wuxi City during 1990–2050. We find that: (1) from 1990 to 2020, the total nitrogen footprint of Wuxi City was in an increasing trend, but the per capita nitrogen footprint was in a decreasing trend. The per capita nitrogen footprint of 22.36 kg capita−1 in 2020 was at a lower level globally. (2) Nr discharge from fossil fuel combustion and Haber-Bosch nitrogen fixation accounted for the main proportion of nitrogen footprint. (3) Dietary choice (Ad), GDP per capita (Ag), urbanization rate (Au), population (P), and fossil energy productivity (Te) were the key factors contributing to the increase of the nitrogen footprint, which resulted in an annual increase of 1.39 %. While nitrogen footprint productivity (Tn), nitrogen use efficiency in crop farming (Tc), and nitrogen use efficiency in animal breeding (Ta) were the key inhibit factors that inhibit the increase of nitrogen footprint, and these factors slow down the annual growth rate of nitrogen footprint by 0.39 %. (4) The continuous growth of nitrogen footprint in the baseline and population growth scenarios will bring more environmental problems and greater environmental governance pressure to Wuxi City, while the sustainable scenario that includes comprehensive means such as economic adaptation and technological improvement is more in line with the requirements of high-quality development in China. Several mitigation measures are then proposed by considering Wuxi's realities from both key impact factors and potential for nitrogen footprint reduction in different scenarios, which can provide valuable policy insights to other cities, especially lakeside cities to mitigate nitrogen footprint.

Low-carbon planning of urban charging stations considering carbon emission evolution characteristics and dynamic demand

As a new generation of transportation, electric vehicles play an important role in carbon-peak targets. The development of electric vehicles needs the support of a charging network, and improper planning of charging stations will result in a waste of resources. In order to expand the charging network of electric vehicles and give full play to the low-carbon and efficient characteristics of electric vehicles, this paper proposed a charging station planning method that considers the characteristics of carbon emission trends. This paper combined the long short-term memory (LSTM) network with the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to predict the carbon emission trend and quantified the correlation between the construction speed of a charging station and the evolution characteristics of carbon emission by Pearson’s correlation coefficient. A multi-stage charging station planning model was established, which captures the dynamic characteristics of the charging demand of the transportation network and determines the station deployment scheme with economic and low-carbon benefits on the spatiotemporal scale. The Pareto frontier was solved by using the elitist non-dominated sorting genetic algorithm. The model and solution algorithm were verified by the actual road network in a certain area of Shanghai. The results showed that the proposed scheme can meet the charging demand of regional electric vehicles in the future, improve the utilization rate of charging facilities, and reduce the carbon emission of transportation networks.

Reconsidering the Long-Term Impacts of Digitalization, Industrialization, and Financial Development on Environmental Sustainability in GCC Countries

Gulf Cooperation Council (GCC) countries have faced environmental challenges in recent decades. This study aims to identify the contribution of digitalization, industrialization, and financial development to the ecological footprint (EF) in GCC countries between 2000 and 2021. The empirical investigation involves estimating the STochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model using the augmented mean group (AMG), common correlated effects mean group (CCEMG) and cross-sectionally augmented autoregressive distributed lag (CS-ARDL) estimators. The findings reveal the existence of long-term linkages between EF and the factors mentioned above. Furthermore, there is evidence that adopting digitalization and information and communication technologies (ICT) improves long-term environmental quality. In contrast, both industrialization and financial development exert detrimental effects on the environment. Finally, the JKS Granger non-causality test revealed that all variables, except financial development, predict environmental degradation in GCC countries. These findings can assist in formulating efficient strategies to reduce ecological degradation and achieve environmental sustainability in GCC countries.

Pollution load estimation and influencing factor analysis in the Tuhai River Basin in Shandong Province of China based on improved output coefficient method.

Estimating the pollution loads in the Tuhai River is essential for developing a water quality standard scheme. This study utilized the improved output coefficient method to estimate the total pollution loads in the river basin while analyzing the influencing factors based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Findings indicated that the projected point source pollution loads for total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (AN) would amount to 3937.22 ton, 335,523.25 ton, and 13,946.92 ton in 2021, respectively. Among these, COD pollution would pose the greatest concern. The primary contributors to the pollution loads were rural scattered life, large-scale livestock and poultry breeding, and surface runoff. Per capita GDP emerged as the most influential factor affecting the pollution loads, followed by cultivated land area, while the urbanization rate demonstrated the least impact.

Analyzing the relationship between energy use for economic development and CO2 emissions, crop, and livestock production in Pakistan by using the extended STIRPAT model

Contemporary human-made activities are responsible for the emission of more than 30 billion tons of carbon dioxide (CO2), a greenhouse gas, into the atmosphere. The current study's primary purpose is to examine the key elements that contribute to the elevated levels of the release of CO2 into the environment in Pakistan. The research used Pakistan's annual data spanning from 1970 to 2020, along with the STIRPAT (Stochastic Impact by Regression on Population, Affluence, and Technology) model. The relationship between the emission of CO2 into the atmosphere and other chosen factors is examined using the ARDL (Auto Regressive Distributive Lag) model and the ECM (Error Correction Model). These models help to establish the credibility of the acquired findings. The paired Granger causality analysis revealed the presence of unidirectional and bidirectional cause-and-effect relationships between the named variable parameters in the activity under investigation. Pakistan must prioritize tackling the fundamental challenges afflicting its farming industry, particularly those pertaining to the efficiency of its livestock and crop production. The novelty of this study comes in its investigation of the interaction between hitherto undiscovered macro-level properties and the emission of CO2 into the environment of Pakistan. The findings may assist policymakers in formulating an environmental and agricultural strategy aimed at promoting the use of sophisticated low-carbon technology.

Institutions and carbon emissions: an investigation employing STIRPAT and machine learning methods

AbstractWe employ an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model combined with the environmental Kuznets curve and machine learning algorithms, including ridge and lasso regression, to investigate the impact of institutions on carbon emissions in a sample of 22 European Union countries over 2002 to 2020. Splitting the sample into two: those with weak and strong institutions, we find that the results differ between the two groups. Our results suggest that changes in institutional quality have a limited impact on carbon emissions. Government effectiveness leads to an increase in emissions in the European Union countries with stronger institutions, whereas voice and accountability lead to a fall in emissions. In the group with weaker institutions, political stability and the control of corruption reduce carbon emissions. Our findings indicate that variables such as population density, urbanization and energy consumption are more important determinants of carbon emissions in the European Union compared to institutional governance. The results suggest the need for coordinated and consistent policies that are aligned with climate targets for the European Union as a whole.

Evaluating the asymmetric effect of patents driven environmental technologies on environmental degradation in the E7 countries: An extended model of STIRPAT

AbstractThis research employed an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) modelling providing original empirical evidence on how technological innovation with the presence of green finance, energy transition vectoring renewable and non‐renewable energy, population, affluence, and financial development affect environmental sustainability measured on the carbon emissions and ecological consequences in E7 during the 1996–2019 period. The study applied mean group (MG), pooled mean group (PMG), and dynamic fixed effects (DFE) after conducting various preliminary tests. The findings predominantly revealed that renewable energy, green finance, and the patent in environmental technologies play an important role in mitigating environmental degradation. However, non‐renewable energy, financial development, population growth, and affluence escalate environmental degradation in the emerging countries under investigation. The outcome of this research proved conclusively that it is vital to introduce the patent on environmental technologies to encourage the collaboration on technology development related to global climate change. Furthermore, enhancing international collaboration in research and technology will positively affect local businesses through utilizing the modern climate change mitigation.

Predicting combined carbon emissions in urban regions considering micro-level enterprise electricity consumption data and macro-level regional data

In the context of “dual carbon” goals, governments need accurate carbon accounting results as a basis for formulating corresponding emission reduction policies. Therefore, this study proposes a combined carbon emission prediction method for urban regions, considering micro-level enterprise electricity consumption data and macro-level regional data. Considering the different applicability of prediction methods and the requirements for the data volume, a region-level carbon emission prediction method based on the long short-term memory neural network is proposed, which takes into account the micro-level electricity–carbon coupling relationship. Additionally, a region-level carbon emission prediction method based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) is proposed, considering the macro-level economic–carbon coupling relationship. The generalized induced ordered weighted averaging method is employed to assign differential weights to micro- and macro-prediction values, yielding regional carbon emission predictions. An empirical analysis is conducted using a key city in the eastern region as an example, analyzing the main influencing factors and predicting carbon emissions based on relevant data from 2017 to 2021, and the accuracy of the models is analyzed and validated.

Four-decades evolutionary development of municipal solid waste management in China: Implications for sustainable waste management and circular economy.

This study collected data on waste generation and management in China between 1979 and 2020 from government statistics and literature and reviewed the development of municipal solid waste (MSW) management in China. The extended stochastic impact by regression on population, affluence and technology (STIRPAT) model was employed to identify the driving forces of MSW generation, and the cointegration analysis showed that economy (0.35, t = -3.47), industrial structure (3.34, t = -20.77) and urbanization (-1.5, t = 5.678) were the significant socioeconomic driving forces in the long run. By employing the framework of evolutionary economics, this study then investigated the internal rules of long-term interaction between socioeconomic factors and MSW management. The results indicate that, in the long run, MSW management development can be viewed as an evolutionary process that includes a continuous adaptation to external socioeconomic factors and the co-evolution of internal institutions and technologies. Adaptation and diversity of institutions and technologies play an important role in achieving sustainable waste management and circular economy (CE). This study offers a novel evolutionary perspective for explaining dynamic changes of MSW management in China, as well as recommendations for emerging economies to achieve sustainable waste management and CE goals.

Analysis of influencing factors of carbon emissions in China based on the STIRPAT model

China, as a major economic power, has been increasing its carbon emissions year after year. Effectively controlling carbon emissions and finding suitable and effective methods to reduce emissions have become the main research themes of current research. The Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model is used in this work to analyze the impact of GDP, population, urbanization, and energy intensity on Chinas carbon emissions from 2003 to 2020. From the output by the SPSS software, it can be illustrated that GDP and energy intensity have more obvious contribution on carbon emission, while urbanization level and population dont. Additionally, as the GDP index increases by a value of one, a 1.220 change will be seen by the carbon emission. Similarly, every one unit change for energy intensity is associated with 0.897 change in carbon emission. Therefore, this paper can consider effective ways to conserve energy and mitigate greenhouse gas emissions from these two aspects, and in this way attain the objective of carbon peaking and carbon neutrality.

Inversed virtual water flow pattern and its influencing factors in Northwest China

Northwest China faces an imbalance between scarce water resources and increasing water use demands for agricultural production and economic growth. However, the shifts in virtual water flow (VWF) patterns in Northwest China and its the driving factors are still unclear. In this study, we employed the Multiregional Input–Output (MRIO) method to investigate the evolution of VWF patterns among Northwest China’s primary, secondary, and tertiary industries and analyze their driving factors through Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The results show that (1) Northwest China changed from 7.24 × 108 m3 net inflow to 175.70 × 108 m3 net outflow from 2002 to 2007. (2) More than 90 % of virtual water of the primary industry was used locally, but both secondary and tertiary industries depended more on virtual water inflow in Northwest China. (3) The virtual water outflow from Northwest China is mostly to the Jing-Jin Region, followed by the Northern Coast, and the main inflow is from the Central Region. (4) Population, economy, and technology affect virtual water from inflow to outflow, and technological development helps to decrease virtual water flow volumes. In conclusion, dramatic decrease of second and third industries inflow is the main reason for the changes from net inflow to net outflow of Northwest China. Meanwhile, it is feasible to increase technological support to further reducing virtual water consumption in primary industry, and strengthen virtual water trades of secondary and tertiary industrial virtual water inflows by promoting population and economic conditions.

Impacts of ICT penetration shaping nonworking time use on indirect carbon emissions: Evidence from Chinese households

People's time use is crucial for carbon emissions related to lifestyles. Increasing the adoption of information and communication technology (ICT) in everyday life changes time-use patterns and, thus, affects emissions. This study proposes a two-way fixed effects model following the stochastic impacts by regression on population, affluence, and technology (STIRPAT) framework to explore the impacts of ICT penetration and household average weekly nonworking time on household indirect emissions. Based on 2010–2018 panel data from the Chinese Family Panel Studies (CFPS), the results show that a 1% increase in household average weekly nonworking time can lead to a 0.169% decrease in emissions by substituting consumption with time. In contrast, a 1% increase in the proportion of family members using a mobile phone or the proportion of family members surfing the internet can increase emissions by 0.107% or 0.127% by increasing electricity and communication consumption. ICT penetration moderates the relationship between nonworking time and emissions, which suggests that ICT amplifies the impact of nonworking time by reducing the consumption of activities and shaping the time-use pattern to be less carbon intensive.

Assessing the impact of changing environment on coal mining water use in China

The unequal distribution of coal and water resources, coupled with efforts to enhance coal production efficiency, has exacerbated the issue of water scarcity in China’s coal mining industry. There is an urgent need to analyze the impact mechanism of external environmental changes on coal mining water use. With partial least square regression and vector autoregressive model under the framework of STIRPAT (stochastic impacts by regression on population, affluence, and technology) model, this paper systematically investigates the impact of population, economic level, energy consumption structure, and water intensity on coal mining water use, as well as the long-term dynamic response of coal mining water use to various variables. Results show a significant positive correlation between the economic level and coal mining water use, and the economic level has the greatest impact on changes in coal mining water use. Water intensity is negatively correlated with water used for coal mining. Decreasing water intensity in coal mining has a rebound effect. Water intensity shows a negative effect in the short term, but gradually disappears in the long run. Energy consumption structure positively correlated with coal mining, but its correlation and influence degree are weak. Promote green and low-carbon energy transformation, water-saving technologies application, and unconventional water utilization, and further improve the sustainable development guarantee capacity of the coal industry.

What impacts do green bonds have on carbon emissions and how? A dynamic spatial perspective in China.

Green bonds are adopted to exclusively finance or refinance green projects and thus is an important policy instrument for sustainable development. The development of these eco-friendly projects benefits the reduction of carbon emission. What impacts do green bonds have on carbon emission intensity and how? This issue needs to be revealed, including the dynamic spatial interactive rules and regional heterogeneity. This is especially true for China, with its vast territory and a short history of green bonds. Different with existing literature, this paper collects the data of the amount of green bond issued in each province in China rather than the policy dummy variable of green bond. The spatio-temporal interactions of both the impact of green bonds and the related mechanisms are studied. A dynamic spatial Durbin model (DSDM) combined with a STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model reveals the negative impacts of green bonds on carbon emissions and the spatio-temporal interactions. On this basis, the mediation model is introduced to clarify the three impact mechanisms of green bonds and find the predominant role of technology mechanism. In addition, different characteristics in spatial interactive rules and impact mechanisms of green bonds are found in various regions of China. Finally, the study proposes some policy recommendations on how to effectively reduce carbon emissions with green bonds.

Socioeconomic driving forces behind air polluting emissions in Mexico.

Air pollution is one of the most severe environmental problems that Mexico is currently facing. The objective of this paper is to quantify the most relevant socioeconomic driving forces behind air polluting emissions and, more specifically, 7 local pollutants in Mexico. We do so in a multilevel version of the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model that accounts for the spatial heterogeneity at the municipal level across the country. The results show that the most relevant variables to determine the emissions of atmospheric pollutants are the population, the harvested area and the number of cars, while technological development helps to mitigate such emissions. The ecological elasticities are, in all cases, smaller than one. Our purpose is to provide quantitative information about these socioeconomic driving forces of air deterioration as a basis to establish some recommendations for environmental policy decision-making.

Study on the Emission Reduction Effect of Green Technology Innovation

Abstract Green technology innovation is one of the important ways to achieve carbon emission reduction targets. Based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and the fixed-effects model, we measured CO2 emissions by region using regional panel data from 2005 to 2021 at provincial level in China and explored the CO2 emission reduction effect. Based on this, we analyzed heterogeneity of green technology innovation and the mechanism of the effect. The results show that green technology innovation has a significant effect on carbon emission reduction, achieving a 5% reduction. Second the CO2 emission reduction effect of green technology innovation varies according to differences in economic, scientific and technological human resources, and environmental management inputs. It shows that green technology innovation has a significant CO2 emission reduction effect in economically developed regions, regions with sufficient scientific and technological human resources, and regions with high environmental management inputs. Third, green technology innovation mainly achieves CO2 emission reduction by improving the level of cleanliness. JEL classification numbers: P28, O14, O15, O18. Keywords: Green technology, Carbon emissions, STIRPAT model, Means to reduce emissions.

Does Economic Policy Uncertainty Cause Environmental Pollution? Fresh Evidence From Developed Countries.

The industrial revolution has dramatically altered the environment and ecosystem. So many scholars have empirically attempted to reveal the most influential anthropogenic factors on environmental degradation. For this purpose, this study examines the leading determinants of CO2 emissions in the context of economic policy uncertainty (EPU) for 14 developed countries within the framework of the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) environmental model from 1997-2018. For empirical modeling, CO2 emission is treated as the dependent variable, which is a strong proxy for environmental degradation. In addition to the GDP per capita, population density, and energy intensity (a proxy for technology), the basic model is extended to include variables such as EPU, renewable energy, trade openness, globalization, and information and communications technology (ICT) index. While the estimation results by the dynamic conditional correlation (DCC) estimator, which are also supported by robustness analysis, suggest that GDP per capita and energy intensity are the main contributors to emission levels, population density has no significant impact on CO2. Furthermore, while renewable energy (in model 2), trade openness (in model 4), and globalization (in model 6) have negative impacts on CO2 emission, technology (in models 5 and 6) and EPU (in model 6) make marginal contributions to CO2.