Economic Impact of Energy Efficiency Policies: A Scenario Analysis
Bibliographic record
Abstract
The number of countries that have pledged to uphold the 2050 decarbonization targets is constantly growing, and many have established strategies and planned related investments for the coming years. The economic impact of decarbonization and energy efficiency policies has become a major topic of discussion in the global effort to mitigate climate change and contain the temperature rise to less than 2 degrees. Previous literature has identified the risks and opportunities of decarbonization policies, especially concerning the rebound effects and the situation that may arise if, due to persistent biases and the costs of fulfilling climate policies, industries were to transfer production to countries where laxer emission constraints are in force. At the core of the 2030 Agenda for Sustainable Development is the Sustainable Development Goals, which are a global call for action regardless of countries’ level of economic development. With Goal 12 on sustainable production and consumption and Goal 14 on climate change mitigation in mind, we provide an economic impact analysis of decarbonization and energy efficiency policies. We compare two scenarios based on the Italian context. The reference scenario is a simulation that shows the development of energy-efficient technologies if the targets set in the national energy strategy were to be met without additional binding targets being added. The policy scenario sees energy efficiency as the principal driver of decarbonization in the presence of a national emissions constraint lasting until 2030, as envisaged by the European Commission. The results confirm that certain risks and opportunities arise from effective policymaking. The effects of decarbonization and energy efficiency policies in the reference scenario would increase final demand by approximately €278.34 billion and the policy scenario would increase it by approximately €380.36 billion by 2030.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".