Periodic Reporting for period 1 - DEEP (Distributional Effects of Environmental Policies)
Período documentado: 2023-05-01 hasta 2025-10-31
Context: Environmental policy is shifting from setting targets to influencing millions of everyday decisions made by households and firms. Yet people and companies start from very different positions: some live where public transport is scarce, some already own clean technologies, some control hydro plants, while others rely on diesel trucks. These “hand-of-the-past” differences shape who can adapt quickly, who pays more, and who ends up better off.
Scientific gap: Existing studies seldom follow choices, wealth effects, and market power over time or combine them in an empirical setting, leaving policymakers without evidence on how today’s rules affect tomorrow’s inequality.
Mission: DEEP – Distributional Effects of Environmental Policy asks: How do existing differences between individuals, households, and firms interact with environmental policies to shape choices and, ultimately, the distribution of costs, benefits, and welfare?
Approach: We are building an economy-wide evidence base that links thirty years of administrative vehicle, energy, and socioeconomic data with detailed policy timelines. Dynamic decision models will then trace adjustment paths, wealth effects, and market-power channels.
Objectives:
1. Electrification of transport. Quantify how location, income, and vehicle portfolios govern electric-car take-up, public-transport substitution, and second-hand-market prices.
2. Renewable energy production and electricity use. Measure how pre-existing generation assets and firm characteristics drive responses to policies that influence investment in renewable energy.
Expected impact: By revealing where policy effectiveness trades off against equity, DEEP will help design climate instruments that accelerate decarbonisation without exacerbating disparities and market power.
Scientific gap: Existing studies seldom follow choices, wealth effects, and market power over time or combine them in an empirical setting, leaving policymakers without evidence on how today’s rules affect tomorrow’s inequality.
Mission: DEEP – Distributional Effects of Environmental Policy asks: How do existing differences between individuals, households, and firms interact with environmental policies to shape choices and, ultimately, the distribution of costs, benefits, and welfare?
Approach: We are building an economy-wide evidence base that links thirty years of administrative vehicle, energy, and socioeconomic data with detailed policy timelines. Dynamic decision models will then trace adjustment paths, wealth effects, and market-power channels.
Objectives:
1. Electrification of transport. Quantify how location, income, and vehicle portfolios govern electric-car take-up, public-transport substitution, and second-hand-market prices.
2. Renewable energy production and electricity use. Measure how pre-existing generation assets and firm characteristics drive responses to policies that influence investment in renewable energy.
Expected impact: By revealing where policy effectiveness trades off against equity, DEEP will help design climate instruments that accelerate decarbonisation without exacerbating disparities and market power.
Data-infrastructure milestones
- National vehicle universe assembled. Every vehicle ever registered in Norway (1990–May 2025) – including scrapped, exported and deregistered units – is now linked to the full chain of ownership transfers and about 32 million odometer readings supplied by NPRA.
- Technical enrichment matched. Commercial new-car prices plus battery and range specifications for all electric and plug-in-hybrid models have been merged under an OFV NDA.
- Power-market backbone constructed. Hour-level load, production and cross-border flow data (ENTSO-E) are integrated with plant-level capacity series from Nordic regulators and weekly reservoir levels for 490 hydro magazines.
- Secure computing environment. A dedicated, access-controlled server has been installed; all raw data, ETL pipelines and estimation code now run inside this environment.
- Incoming expansions. Final legal drafts and preparations cover (i) about 2.5 million household smart-meter time series from Elhub (hourly, 2019-present), (ii) microdata for the National Travel Survey waves 2001–2021 (NPRA), (iii) a 17-year archive of used-car listings from Finn.no and (iv) individual and household socioeconomic and demographic panel from Statistics Norway. The ETL pipeline is modular, so ingestion will start as soon as access is signed.
Modelling and estimation progress
- Dynamic vehicle-choice model with market power. Estimated on two million households. Registration taxes or subsidies based on vehicle attributes plus fuel taxation approximate first-best when firms are competitive. Introducing oligopoly mark-ups shows producer pricing already exceeds social marginal cost, and a driving-based tax alone lowers welfare by shrinking new-car sales. Surplus losses from kilometre taxes fall about 35 percent more on rural than on urban households.
- Modal-substitution project. Theoretical and empirical frameworks are complete; large-sample choice sets generated; estimation pending Travel Survey microdata.
- Electric-truck uptake study. Grant-level data from ENOVA’s subsidy scheme are matched to the firm census; hazard-model code is ready for estimation once fleet-technical variables are appended.
- EV-charging and renewables interaction. First regressions show household EV charging is highly price-inelastic and its evening peak only partly overlaps the hourly wind-generation profile. This implies complex effects on wind-power profitability. Additional smart-meter and market-microstructure data are being prepared to simulate the feedback loop between EV demand, hourly prices and investment incentives across producer types.
Team-based scientific capacity
Two post-docs (start M14) lead the modal-choice and electric-truck strands. Two PhD students work on welfare and charging studies, with a third joining in Month 30. Three research assistants maintain the secure-server ETL workflows and first-pass analytics.
Overall status versus objectives
The multi-sector database is operational, four analytical strands are active, and the dynamic evaluation models are calibrated, keeping the project on track to deliver its scientific objectives for transport electrification and renewable-energy policy by Month 60.
- National vehicle universe assembled. Every vehicle ever registered in Norway (1990–May 2025) – including scrapped, exported and deregistered units – is now linked to the full chain of ownership transfers and about 32 million odometer readings supplied by NPRA.
- Technical enrichment matched. Commercial new-car prices plus battery and range specifications for all electric and plug-in-hybrid models have been merged under an OFV NDA.
- Power-market backbone constructed. Hour-level load, production and cross-border flow data (ENTSO-E) are integrated with plant-level capacity series from Nordic regulators and weekly reservoir levels for 490 hydro magazines.
- Secure computing environment. A dedicated, access-controlled server has been installed; all raw data, ETL pipelines and estimation code now run inside this environment.
- Incoming expansions. Final legal drafts and preparations cover (i) about 2.5 million household smart-meter time series from Elhub (hourly, 2019-present), (ii) microdata for the National Travel Survey waves 2001–2021 (NPRA), (iii) a 17-year archive of used-car listings from Finn.no and (iv) individual and household socioeconomic and demographic panel from Statistics Norway. The ETL pipeline is modular, so ingestion will start as soon as access is signed.
Modelling and estimation progress
- Dynamic vehicle-choice model with market power. Estimated on two million households. Registration taxes or subsidies based on vehicle attributes plus fuel taxation approximate first-best when firms are competitive. Introducing oligopoly mark-ups shows producer pricing already exceeds social marginal cost, and a driving-based tax alone lowers welfare by shrinking new-car sales. Surplus losses from kilometre taxes fall about 35 percent more on rural than on urban households.
- Modal-substitution project. Theoretical and empirical frameworks are complete; large-sample choice sets generated; estimation pending Travel Survey microdata.
- Electric-truck uptake study. Grant-level data from ENOVA’s subsidy scheme are matched to the firm census; hazard-model code is ready for estimation once fleet-technical variables are appended.
- EV-charging and renewables interaction. First regressions show household EV charging is highly price-inelastic and its evening peak only partly overlaps the hourly wind-generation profile. This implies complex effects on wind-power profitability. Additional smart-meter and market-microstructure data are being prepared to simulate the feedback loop between EV demand, hourly prices and investment incentives across producer types.
Team-based scientific capacity
Two post-docs (start M14) lead the modal-choice and electric-truck strands. Two PhD students work on welfare and charging studies, with a third joining in Month 30. Three research assistants maintain the secure-server ETL workflows and first-pass analytics.
Overall status versus objectives
The multi-sector database is operational, four analytical strands are active, and the dynamic evaluation models are calibrated, keeping the project on track to deliver its scientific objectives for transport electrification and renewable-energy policy by Month 60.
DEEP moves the study of distributional effects from static, single-policy snapshots to a dynamic, economy-wide perspective built on linked administrative microdata.
On the consumer side, the project delivers the first model that (i) lets households decide jointly over consumption, saving, vehicle purchase and driving, (ii) captures intertemporal adjustment, and (iii) tracks how resale values feed back into life-cycle wealth. Early estimates already show that taxes on driving impose about thirty-five per cent larger welfare losses on rural than on otherwise similar urban households—an equity-efficiency trade-off invisible in less granular frameworks. Forthcoming counterfactuals will quantify how alternative mixes of subsidies and taxes reshape the distribution of prices, car ownership, emissions and welfare.
On the supply side, DEEP develops the first plant-level model in which renewable investment, operation and endogenous market power all hinge on legacy capacity. Combining hourly power-system records with firm-level capacity histories will reveal how storage, intermittent generation and policy instruments interact. Counterfactuals will trace how changing subsidy rates or certificate lengths alters entry, price volatility and long-run concentration in the electricity market.
Both modelling frameworks become reusable building blocks—a high-dimensional choice model with state-dependent resale values and an investment-operation model for mixed generation—that researchers in other countries can calibrate with access to suitable data. Completing the analyses requires (a) individual and household panel data from Statistics Norway, (b) household smart-meter microdata from Elhub, (c) National Travel-Survey microdata, and (d) continued high-performance computing time for forward-looking simulations with rich state spaces. Access agreements for the data are in final legal review, and the dedicated secure server installed this year provides the necessary storage and processing headroom.
By jointly modelling heterogeneous households, dynamic incentives and system-level feedbacks, DEEP is set to redefine empirical research on the distributional consequences of environmental policy and to inform a broad strand of future work on inequality dynamics under evolving climate measures.
On the consumer side, the project delivers the first model that (i) lets households decide jointly over consumption, saving, vehicle purchase and driving, (ii) captures intertemporal adjustment, and (iii) tracks how resale values feed back into life-cycle wealth. Early estimates already show that taxes on driving impose about thirty-five per cent larger welfare losses on rural than on otherwise similar urban households—an equity-efficiency trade-off invisible in less granular frameworks. Forthcoming counterfactuals will quantify how alternative mixes of subsidies and taxes reshape the distribution of prices, car ownership, emissions and welfare.
On the supply side, DEEP develops the first plant-level model in which renewable investment, operation and endogenous market power all hinge on legacy capacity. Combining hourly power-system records with firm-level capacity histories will reveal how storage, intermittent generation and policy instruments interact. Counterfactuals will trace how changing subsidy rates or certificate lengths alters entry, price volatility and long-run concentration in the electricity market.
Both modelling frameworks become reusable building blocks—a high-dimensional choice model with state-dependent resale values and an investment-operation model for mixed generation—that researchers in other countries can calibrate with access to suitable data. Completing the analyses requires (a) individual and household panel data from Statistics Norway, (b) household smart-meter microdata from Elhub, (c) National Travel-Survey microdata, and (d) continued high-performance computing time for forward-looking simulations with rich state spaces. Access agreements for the data are in final legal review, and the dedicated secure server installed this year provides the necessary storage and processing headroom.
By jointly modelling heterogeneous households, dynamic incentives and system-level feedbacks, DEEP is set to redefine empirical research on the distributional consequences of environmental policy and to inform a broad strand of future work on inequality dynamics under evolving climate measures.