Impact of environmental policy stringency on sectoral GHG emissions: evidence from Finland and Sweden by nonlinear quantile-based methods

ABSTRACT

The growing societal concern regarding environmental matters has led to the implementation of many environmental measures intended to protect the environment and address global warming by lessening emissions and mitigating climate change. In line with this movement, this study scrutinizes the impact of these environmental measures on greenhouse gas (GHG) emissions to analyze the cases of Finland and Sweden. More specifically, the study employs the Environmental Policy Stringency (EPS) index as a proxy for environmental measures, explores sector-specific GHG emissions by employing nonlinear quantile-based methodologies (including quantile-on-quantile regression and Granger causality-in-quantiles methods as the primary model and quantile regression for robustness checking) spanning the period from 1991/Q1 to 2020/Q4. The findings show that: (i) EPS lessens GHG emissions from fuel exploitation, industrial combustion, and the power industry sector at lower and middle quantiles in Finland and Sweden; (ii) EPS decreases GHG emissions from processes, transportation, and waste sectors in Finland but increases them in Sweden at higher quantiles; (iii) EPS leads to an increase in GHG emissions from the agriculture and construction sectors at higher quantiles; (iv) EPS has a causal effect on sector-specific GHG emissions across different quantiles; (v) the robustness of the findings is largely confirmed. Hence, the study underscores the varying impacts of EPS on sectoral GHG emissions based on quantiles, sectors, and countries, emphasizing the need for policymakers to adopt environmental policies to comprise these differences and adjust the policy framework accordingly.

KEYWORDS:

• Environmental policy stringency
• GHG emissions
• sectors
• Finland
• Sweden
• quantile-based methods

Disclosure statement

No potential conflict of interest was reported by the author(s).

Authors’ contributions

The authors have contributed equally to this work. All authors read and approved the final manuscript.

Acronyms

ARDL	=	Autoregressive Distributed Lags
BDS	=	Broock, Scheinkman, Dechert, and LeBaron
CO2	=	Carbon Dioxide
CS-ARDL	=	Cross-Sectional ARDL
EDGAR	=	Emissions Database for Global Atmospheric Research
EF	=	Ecological Footprint
EKC	=	Environmental Kuznets Curve
EPS	=	Environmental Policy Stringency
ETS	=	Emission Trading System
G-7	=	Group of Seven
GHG	=	Greenhouse Gas
GQ	=	Granger Causality-in-Quantiles
OECD	=	Organization for Economic Co-operation and Development
PMG	=	Pooled Mean Group
QQ	=	Quantile-on-Quantile Regression
QR	=	Quantile Regression

Availability of data and materials

Data will be made available on request.

Consent for publication

The authors are willing to permit the Journal to publish the article.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/13504509.2024.2339509

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.