The Interaction Between Government Policies and Emission Cuts: Evidence from Emerging and Advanced Economies
Bibliographic record
Abstract
Government policies such as subsidies, environmentally-related research and development expenditures and technological incentives play crucial role in mitigating the implications of climate change. Our study investigates the role of governments in scaling up climate transition in advanced and emerging countries. We employ Panel Augmented Mean Group Estimator to find the long-term relationship between carbon intensity and various government policies. Covering 2010–2021 period, the data of 18 countries were included in the estimations. Carbon intensity, as a measure of climate change, is proxied by carbondioxide (CO2) emissions per GDP while government policy is represented with 3 indicators: i. Total fossil fuel support as a % of tax revenue, ii. Environmentally-related government research and development (R&D) budget as a % of total government R&D budget and iii. Development of environment-related technologies as a % of all technologies. The results of the estimation covering all countries in the dataset indicate that development of environment-related technologies is positively interrelated with CO2 emissions per GDP, contrary to our ex-ante expectations. It is inferred that the development of technologies does not necessarily reflect their level of usage. As for emerging countries, there is a mixed pattern in the interrelation between climate change and explanatory variables related to government policies. This is partly because the environmental policies and regulations in emerging countries are not sufficiently entrenched to achieve intended results and there appears to be a lack of effective data reporting. On the other hand, the results indicate that in advanced countries, fossil fuel subsidies are positively interrelated with CO2 emissions per GDP in the long-term, compatible with our ex-ante expectations on the deteriorating impact of fossil fuel subsidies on climate change. Checking country-based estimation results, it is striking that in advanced countries with higher income levels, development of environment-related technologies does not contribute to limit climate change. This finding confirms the difference between exporters and end-users of environmental technologies. We draw attention to the export factor where the exporter bears the environmental damages of production process while not thoroughly benefitting from the environmental advantages of the technology. US, UK, Canada, Japan and Germany, where environment-related technologies are not interrelated with carbon intensity, might be considered as predominant technology-exporters. Finally, we propose additional policy implications regarding the role of governments in emission cuts. Government support might range from grants, subsidies, feed-in-tariffs, tax exemptions, direct tax credits, credit guarantees and other kind of incentive schemes for decarbonization technology investments. Along with financial support, governments might also support decarbonization via creating an enabling regulatory landscape for the development of climate and environment-related technologies as well as removing information asymmetries pertaining to climate investments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".