Abstract
Climate change is increasingly becoming a pressing issue, and a substantial reduction of
greenhouse gas emissions needs to be realised. For this transition to a green economy,
increased investments are required. This thesis studies this transformation to a green economy, by studying (1) firm investment inefficiencies which result in sub-optimal private investments and (2) the spatial distributional impacts of this transition and the required green investments. The first part of this thesis studies the firm investment decision for green technologies.
The energy efficiency (EE) gap among firms in the Netherlands is measured using a questionnaire that is sent to firms in the Netherlands. We find that most firms in our sample (70%) have invested in the past, where the median firm has saved 10% of its energy use. However, the remaining profitable EE investment opportunities can still result in another 15% of energy savings at the median firm. Uncertainty about future policies is found to be the most important barrier to invest more, followed by lock-ins in current equipment, and uncertainty about energy costs. We find that few firms expect that they have to fire employees when investing in EE.
We investigate the interaction between labour market inefficiencies and the delay in investments in green technologies, by setting up a labour market model. Imperfect labour markets and difficulties in finding employees results in skill mismatch, which negatively affects the expected profitability of new, green technologies, causing a delay in green investments and higher emission intensity in production. In addition, this slower diffusion results in workers with green skills being locked in ‘dirtier’ jobs. We show that retraining policy can be used in addition to climate change policy to alleviate negative effects on labour market outcomes.
In the second part of this thesis, we investigate the spatial distribution of economic activity and the interaction of energy dependency and green investments on regional economic outcomes.
We investigate which regions in Europe are most sensitive to changes in fossil fuel prices in terms of their competitive position of the industry sector. European regions are resilient to global coal price increases, whereas they are vulnerable to gas price shocks. Efficiency improvements in the use of fossil fuels can indeed reduce these gas price vulnerabilities. However, when competitors become more efficient, vulnerability to fossil fuel price shocks may increase. We also show that decarbonising upstream sectors like electricity generation in a European setting can increase resilience of downstream regional industries.
Comparing an input-output (IO) model and a computable general equilibrium (CGE) model with different closures, we evaluate the model choice used to estimate the impact of
green investments on the regional distribution of economic activity. Based on a numerical example, we draw implications for those using regional models. When the production
capacity in the economy can expand in the short-run, IO models could underestimate
the employment impact. Priority should thus be given to sensitivity analyses in terms of the model specification instead of adjusting specific substitution parameters. Policy makers using results from such models should be aware of the underlying assumptions as policy implications can be different depending on the model used.
The dissertation shows that increasing investment and the diffusion of green technologies
requires governments to not only set up policies that address the negative
externality of energy use, but also tackle market failures or inefficiencies hampering this
diffusion. Different geographical distributions of traditional vs. renewable energy and
historical specialisation in regional economies, demand a regional focus. Furthermore,
this thesis shows that using mixed methods and applying them to answer new questions
can result in new perspectives and in comprehensive, useful evidence for policymakers.
Original language | English |
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Qualification | PhD |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 7 May 2025 |
Print ISBNs | 9789036107907 |
DOIs | |
Publication status | Published - 7 May 2025 |