Policy Coherence for Carbon Neutrality: The Fiscal, Monetary, and Energy Strategies for Sustainable Development Goals

ABSTRACT This study examines the association between fiscal policy, monetary policy, renewable energy use, natural resource rents, and economic growth with CO 2 emissions in G‐20 economies from 1990 to 2023. Using annual panel data specified predominantly in logarithmic form, with monetary‐policy variables retained in levels, we apply the Method of Moments Quantile Regression (MMQR) to assess heterogeneous long‐run relationships across the emissions distribution. Robustness is evaluated through Common Correlated Effects Mean Group (CCEMG) and Augmented Mean Group (AMG) estimators. Three main results emerge. First, natural resource rents and fiscal spending (% of GDP) display positive elasticities with CO 2 emissions across all quantiles. Second, renewable energy exhibits negative elasticities, with stronger emission‐reducing effects in higher‐emission quantiles. Third, tighter monetary conditions are associated with lower emissions, whereas economic growth is positively linked with CO 2 levels throughout the distribution. Taken together, the findings suggest that carefully targeted fiscal restructuring, expanded renewable‐energy deployment, and prudent macro‐financial coordination can support long‐run decarbonization efforts in the G‐20, while recognizing that the results describe associations rather than causal effects.

Authors

Institutions

Publication Details

Journal
Sustainable Development
Published
2025-12-12
DOI
https://doi.org/10.1002/sd.70564
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Policy Coherence for Carbon Neutrality: The Fiscal, Monetary, and Energy Strategies for Sustainable Development Goals

E. Marian Scott, Mo Li
Sustainable Development
Energy, Environment, Economic Growth
article

Policy Coherence for Carbon Neutrality: The Fiscal, Monetary, and Energy Strategies for Sustainable Development Goals

E. Marian Scott, Mo Li
article en

Abstract

ABSTRACT This study examines the association between fiscal policy, monetary policy, renewable energy use, natural resource rents, and economic growth with CO 2 emissions in G‐20 economies from 1990 to 2023. Using annual panel data specified predominantly in logarithmic form, with monetary‐policy variables retained in levels, we apply the Method of Moments Quantile Regression (MMQR) to assess heterogeneous long‐run relationships across the emissions distribution. Robustness is evaluated through Common Correlated Effects Mean Group (CCEMG) and Augmented Mean Group (AMG) estimators. Three main results emerge. First, natural resource rents and fiscal spending (% of GDP) display positive elasticities with CO 2 emissions across all quantiles. Second, renewable energy exhibits negative elasticities, with stronger emission‐reducing effects in higher‐emission quantiles. Third, tighter monetary conditions are associated with lower emissions, whereas economic growth is positively linked with CO 2 levels throughout the distribution. Taken together, the findings suggest that carefully targeted fiscal restructuring, expanded renewable‐energy deployment, and prudent macro‐financial coordination can support long‐run decarbonization efforts in the G‐20, while recognizing that the results describe associations rather than causal effects.

Sustainable Development
University of Chicago (US), Guangdong University of Finance (CN)
Openalex Percentile: Top 53%
Energy, Environment, Economic Growth
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.