Technological change and the rebound effect in the STIRPAT model: A critical view

Vélez-Henao, Johan-Andrés; Vivanco, David Font; Hernández-Riveros, Jesús-Antonio

doi:10.1016/j.enpol.2019.03.044

Cited by 114 publications

(49 citation statements)

References 112 publications

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“…(2019). Agglomeration of the population can lead to higher emissions, which is consistent with many existing studies on population economics and regional economics (Bhattacharya et al., 2017; Vélez‐Henao et al., 2019; Yang et al., 2018). For the positive local and spatial spillover effects of the development of service industry, they coincide with Lin and Zhang (2017), Liu, Xiao, et al.…”

Section: Resultssupporting

confidence: 87%

“…(2018), and Vélez‐Henao et al. (2019). The basic STIRPAT for cross‐sectional setting is:

{I}_{i}=a{P}_{i}^{b}{A}_{i}^{c}{T}_{i}^{d}{e}_{i\hspace*{.5em}}

where i stands for the i th region (or provincial area, in this study); I, P, A, and T represent environmental impact, population, affluence, and technology, respectively;

{e}_{i}

is the error term.…”

Section: Methodology and Datamentioning

confidence: 98%

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Heterogeneous Spatial Effects of FDI on CO₂ Emissions in China

et al. 2022

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As the world’s largest CO2 emitter, China’s emission reduction is the key to mitigating global climate change. Global investment capital, as a significant influencing factor of emissions, greatly contributes to China’s economic growth and stimulates frequent interprovincial trade since the reform and opening. However, the spatial correlation of foreign investment and CO2 emissions across Chinese provinces is rarely considered. This paper explores the role of foreign direct investment (FDI) in emission reduction in China during 2004–2015 using the spatial Durbin economic models with two‐way fixed effects. Then, the partial differential equations decomposition of the local and spatial spillover effects is conducted for capturing the marginal effects of influencing factors. Results show that FDI contributes to emission reduction for the whole country, whereas interprovincial transmission and intra‐provincial transmission show significant heterogeneity. Specifically, FDI increases emissions in host areas, while FDI provides positive environmental externalities to adjacent areas through spatial spillovers. Our findings provide a new perspective to explain the coexistence of the “Pollution Haven” and “Pollution Halo” theories in a country. Also, the exploration of spatial spillover effects of FDI on emissions can provide a reference for other countries with rising trade and investment flows to promote collaborative emission reduction.

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Section: Resultssupporting

confidence: 87%

“…(2018), and Vélez‐Henao et al. (2019). The basic STIRPAT for cross‐sectional setting is:

{I}_{i}=a{P}_{i}^{b}{A}_{i}^{c}{T}_{i}^{d}{e}_{i\hspace*{.5em}}

where i stands for the i th region (or provincial area, in this study); I, P, A, and T represent environmental impact, population, affluence, and technology, respectively;

{e}_{i}

is the error term.…”

Section: Methodology and Datamentioning

confidence: 98%

Heterogeneous Spatial Effects of FDI on CO₂ Emissions in China

et al. 2022

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“…For analyzing the effect, we employed the IPAT framework (Ehrlich and Holdren, 1971). The estimation schema is designed depending on the available literature on the EKC hypothesis and IPAT modeling (Paramati et al, 2017;Vélez-Henao et al, 2019;Sinha and Sengupta, 2019). The primary reason for using the IPAT framework for this study is the capability of this framework to capture the evolutionary environmental impact (I) of population (P), economic affluence (A), and technological advancement (T).…”

Section: Empirical Framework and Datamentioning

confidence: 99%

Technology policy and environmental quality at crossroads: Designing SDG policies for select Asia Pacific countries

Sinha¹,

Sengupta²,

Saha

2020

Technological Forecasting and Social Change

121

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Since the inception of Sustainable Development Goals (SDGs), the Asia Pacific countries are facing difficulties in attaining the SDG objectives, as maintaining the environmental quality has been a challenge for them. In this study, we have revisited the technology policies of these countries, and in doing so, we have tried to address the problem of environmental degradation, while addressing the issues of sustainable economic growth, clean and affordable energy, and quality education. In this pursuit, we have designed two indices for environmental degradation and technological advancement, and then analyzed the association between them following the Environmental Kuznets Curve (EKC) hypothesis. Following IPAT framework, and by using quantile approach, over a period of 1990-2017, we have found that the turnaround points of EKCs rise with the rise in quantiles, i.e. quantiles with low pollutions are having turnaround points within sample range, whereas quantiles with high pollutions are having turnaround points outside sample range. Using Rolling Window Heterogeneous Panel Causality test, unidirectional causality has been found running from technological advancement to environmental degradation.Following the results obtained from the analysis, we have tried to address the objectives of SDG

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“…We selected several factors to represent T. According to Dietz and Rosa [42], T is not a single factor but comprises many separate factors that influence the environment. In a few studies, T was interpreted as the residual term [45,46]. In some other studies [44,47,48], T may be represented by multiple different variables, including energy intensity, energy structure, urbanization, and industrialization.…”

Section: Spatial Panel Econometric Modelmentioning

confidence: 99%

The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis

et al. 2020

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As the largest carbon emitter in the world, China is confronted with great challenges of mitigating carbon emissions, especially from its construction industry. Yet, the understanding of carbon emissions in the construction industry remains limited. As one of the first few attempts, this paper contributes to the literature by identifying the determinants of carbon emissions in the Chinese construction industry from the perspective of spatial spillover effects. A panel dataset of 30 provinces or municipalities from 2005 to 2015 was used for the analysis. We found that there is a significant and positive spatial autocorrelation of carbon emissions. The local Moran’s I showed local agglomeration characteristics of H-H (high-high) and L-L (low-low). The indicators of population density, economic growth, energy structure, and industrial structure had either direct or indirect effects on carbon emissions. In particular, we found that low-carbon technology innovation significantly reduces carbon emissions, both in local and neighboring regions. We also found that the industry agglomeration significantly increases carbon emissions in the local regions. Our results imply that the Chinese government can reduce carbon emissions by encouraging low-carbon technology innovations. Meanwhile, our results also highlight the negative environmental impacts of the current policies to promote industry agglomeration.

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Technological change and the rebound effect in the STIRPAT model: A critical view

Cited by 114 publications

References 112 publications

Heterogeneous Spatial Effects of FDI on CO₂ Emissions in China

Heterogeneous Spatial Effects of FDI on CO₂ Emissions in China

Technology policy and environmental quality at crossroads: Designing SDG policies for select Asia Pacific countries

The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis

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Technological change and the rebound effect in the STIRPAT model: A critical view

Cited by 114 publications

References 112 publications

Heterogeneous Spatial Effects of FDI on CO2 Emissions in China

Heterogeneous Spatial Effects of FDI on CO2 Emissions in China

Technology policy and environmental quality at crossroads: Designing SDG policies for select Asia Pacific countries

The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis

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Heterogeneous Spatial Effects of FDI on CO₂ Emissions in China

Heterogeneous Spatial Effects of FDI on CO₂ Emissions in China