Journal ArticleDOI
Can environmental innovation facilitate carbon emissions reduction? Evidence from China
TLDR
Wang et al. as mentioned in this paper used a system generalized method of moments (SGMM) technique to estimate the effect of environmental innovation on carbon emissions in China and evaluated the effect on carbon emission reduction of China's initial carbon emissions trading (CET) scheme.About:
This article is published in Energy Policy.The article was published on 2017-01-01. It has received 526 citations till now. The article focuses on the topics: Emissions trading & Greenhouse gas.read more
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Consumption-based carbon emissions and International trade in G7 countries: The role of Environmental innovation and Renewable energy
TL;DR: To explore the unidentified determinants of CO2 emissions in G7 countries from 1990 to 2017, this study uses second-generation panel co-integration methodologies and confirms a stable long-run relationship amongCO2 emissions, trade, income, environmental innovation and renewable energy consumption.
Journal ArticleDOI
The effect of innovation on CO2 emissions of OCED countries from 1990 to 2014.
Claudia Nyarko Mensah,Xingle Long,Kofi Baah Boamah,Isaac Asare Bediako,Lamini Dauda,Muhammed Salman +5 more
TL;DR: It is asserted that improvement in GDP per capita leads to the rise in CO2 in most OECD economies, although mitigate emissions in few OECDs; hence, the economic-EKC model is not valid for most economies.
Journal ArticleDOI
Does technological innovation reduce CO2 emissions?Cross-country evidence
Yang Chen,Chien-Chiang Lee +1 more
TL;DR: Based on the data of BP Statistical Review of World Energy, KOF Globalization Index, and the World Development Indicators, the authors explores the impact of technological innovation on CO2 emissions in a panel of 96 countries over the period 1996-2018 with spatial econometric models.
Journal ArticleDOI
R&D intensity and carbon emissions in the G7: 1870–2014
TL;DR: The authors examined the effect of research and development (R&D) intensity on carbon dioxide (CO2) emissions in the Group of Seven (G7) countries since the nineteenth century using a non-parametric panel data model.
Journal ArticleDOI
The impact of innovation and technology investments on carbon emissions in selected organisation for economic Co-operation and development countries
TL;DR: In this article, a system-generalized method of moments (GMM) analysis was conducted to investigate how innovation and technology investments influence carbon emissions in selected OECD economies from 2000 to 2014.
References
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Journal ArticleDOI
The central role of the propensity score in observational studies for causal effects
TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
Report SeriesDOI
Initial conditions and moment restrictions in dynamic panel data models
Richard Blundell,Stephen Bond +1 more
TL;DR: In this paper, two alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator are presented. But both estimators require restrictions on the initial conditions process.
Journal ArticleDOI
Another look at the instrumental variable estimation of error-components models
Manuel Arellano,Olympia Bover +1 more
TL;DR: In this paper, a framework for efficient IV estimators of random effects models with information in levels which can accommodate predetermined variables is presented. But the authors do not consider models with predetermined variables that have constant correlation with the effects.
Journal ArticleDOI
Large sample properties of generalized method of moments estimators
Journal ArticleDOI
Unit root tests in panel data: asymptotic and finite-sample properties
TL;DR: In this article, the authors consider pooling cross-section time series data for testing the unit root hypothesis, and they show that the power of the panel-based unit root test is dramatically higher, compared to performing a separate unit-root test for each individual time series.
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