The increasing integration of offshore wind energy into volatile electricity markets necessitates more frequent curtailment as a response to negative pricing conditions. Although curtailment can mitigate short-term financial losses, its long-term impacts on offshore wind turbine (OWT) degradation remain largely unaddressed. This thesis investigates the hidden costs associated with curtailment, focusing specifically on the degradation of critical turbine components, including the gearbox, blades, and support structures. By developing and comparing optimisation models, this research evaluates the effectiveness of coordinated curtailment strategies that integrate asset health considerations with market-driven objectives.

Three distinct optimisation frameworks are proposed: a baseline reflecting current market-driven curtailment practices, a centralised strategy optimising asset health and financial performance collectively, and a decentralised approach applying the Alternating Direction Method of Multipliers (ADMM) to address fragmented stakeholder interests. Results indicate significant benefits from centralised coordination, showing a notable reduction in investment costs and a substantial increase in overall profit, as well as improved return on investment. However, contractual complexities and data-sharing constraints can hinder centralised implementation, highlighting the potential advantages of a decentralised strategy.

The findings underscore the value of internalising asset health into curtailment decisions, demonstrating clear financial and operational improvements. Looking ahead, the value of centralised coordination is expected to grow as electricity markets experience increasing negative price events and greater volatility, driven by a higher share of variable renewable energy sources. Consequently, internalising asset health in curtailment strategies will become increasingly essential for future energy systems.

Climate change presents a significant global challenge, prompting governments to adopt a range of
mitigation strategies, most notably, carbon pricing schemes. However, such policies are often met
with concern over carbon leakage, where emissions reductions in one country are offset by increases
elsewhere, potentially undermining the environmental effectiveness of national efforts.
This thesis investigates the relationship between carbon pricing policies and national carbon leakage
rates over the period 1995–2018. Drawing on data from the TECO2 dataset and the International and
Sectoral Variation in Industrial Energy Prices 1995–2015, and applying the electricity price elasticity
methodology introduced by Misch and Wingender (2024), this study calculates annual carbon
leakage rates for each country. In doing so, it extends previous research, such as the Misch and
Wingender and Sato et al (2019).
A key contribution of this thesis is its novel application of the Carbon Pricing Dashboard, a dataset
that remains underutilized in empirical research. This study aims to demonstrate its potential for
carbon leakage analysis. Another important contribution is the attempt to extend the methodology
developed by Misch and Wingender by applying it not only to CO₂ emissions, as in the original study,
but also to broader greenhouse gas emissions. This provides insight into the scope and limitations of
their approach when adapted to different types of emissions data. Finally, this thesis contributes to
the ongoing policy discussion on carbon pricing by linking yearly carbon leakage rates to country-
specific pricing policy variables, such as carbon price levels and policy coverage. In doing so, it
investigates whether systematic relationships exist between national carbon pricing strategies and
the extent of carbon leakage.
The results indicate that carbon pricing policies explain a meaningful share of the variation in leakage
rates across countries, though country-specific fixed effects account for an even larger portion,
suggesting that structural or institutional factors may play a more dominant role. Notably, the
analysis reveals a negative relationship between carbon pricing and leakage rates, implying that more
ambitious pricing policies may actually help reduce leakage. This finding challenges the prevailing
narrative in the literature, which often assumes that stricter carbon pricing increases leakage and
harms competitiveness. It suggests that policymakers might be able to adopt stronger carbon pricing
measures without significant economic drawbacks. Additionally, the study finds no strong evidence
that coordinated international carbon pricing efforts lead to greater leakage reductions compared to
unilateral national policies, raising questions about the added value of formal international
alignment in this context. However, these results should be interpreted with caution. The method
used to estimate leakage rates may introduce bias, and further validation is necessary.
That said, the research has some limitations. Most notably, the fixed effects model could not
incorporate time fixed effects due to data constraints, raising the risk that some time-related
variation may have been wrongly attributed to policy variables. This limitation may partly explain the
unexpected direction of some results.
By extending and refining the methodology developed by Misch and Wingender (2024), this thesis
offers new empirical insights into the drivers of carbon leakage and contributes to the policy debate
on the design and efficacy of national carbon pricing frameworks. In doing so, it also provides a more
detailed methodological reflection on the assumptions and limitations inherent in price elasticity-
based leakage estimation.

Most developed countries generate more CO2 emissions for their imports than they generate to produce exports, while most developing countries generate more CO2 emissions to produce exports than they generate foreign emissions to produce their imports. Most developed countries ‘import’ CO2 emissions in net terms, while most developing countries ‘export’ CO2 emissions in net terms. There has been a negative cross-country correlation between net CO2 emission transfers and per-capita income.

Two interpretations exist. On one side, the emission transfers between the developed and developing economies may stem from the shift of CO2-intensive production from the developed to the developing economies. On the other side, the size and direction of international emission transfers may also be determined by international technology differences: in the presence of technology differences, the exchange of identical products of similar monetary value would imply emission transfers. This dissertation uses Environmentally-Extended Multi-Regional Input-Output (EE MRIO) analysis to study the international emission transfers and regression analysis to assess whether the economy-emission decoupling observed in developed countries is indeed valid.

Additionally, there arises a need to explore specific mechanisms to address the increase in CO2 emissions. Carbon tax represents one such mechanism. A carbon tax generates revenue and creates incentives for behavioral changes among both producers and consumers. However, in the short run, a carbon tax leads to price increases and disproportionately affects poor households. This dissertation combines EE MRIO and microsimulation analysis to analyze the distributional impacts of carbon tax reform.@en

Hydrogen is key to decarbonising hard-to-abate industries where electrification has a limited role to play. With a cleaner carbon footprint and versatility with existing industrial processes, hydrogen can be used as a fuel in these energy-intensive industries. As the hydrogen industry is still in its initial phases many researchers have estimated its economic impacts. Most of the current research is focused on the employment market. This study evaluates the economic impact of the emerging green hydrogen industry in India both in terms of employment creation and contribution to GDP growth. The study also
quantifies the savings in imports caused by switching from fossil fuel to green hydrogen in different industries. The main objective of this study is to verify the projections of the National Green Hydrogen Mission(NGHM) of India which aims to produce 5 million metric tons (MMT) of hydrogen per annum by 2030. Leontief’s input-output model is used to evaluate the economic impacts.

The study revealed that there would be an employment creation of 410570 FTE jobs and 4838 M USD contribution to GDP for every gigawatt(GW) scale hydrogen plant installed. Additionally, there would also be import savings of around 1 B USD per annum for a GW-scale plant. It is found that the amount of imports of machinery and equipment significantly affects the impact indicators. If half of the machinery and equipment is imported the contribution to GDP is reduced by 50% whereas the employment creation and GHG emissions are reduced by 30% when compared to the all the machinery is domestically manufactured scenario. The projections of India’s NGHM are evaluated and found comparable with those of the government projections. Overall, the establishment of the green hydrogen industry in India is found to have a positive impact on the economy by significantly boosting the national GDP and employment creation.

This Master's thesis investigates the impact of the European Emission Trading System (EU ETS) on Carbon Leakage. It uses regression analysis of panel data to find out whether there is a link between the EU allowance price and trade flows. The study furthermore provides extensive recommendations to policymakers regarding potential improvement of the EU ETS and the overall competitiveness of European Industry.

The European Union Emissions Trading System (EU ETS) and the Carbon Border Adjustment Mechanism (CBAM) are important mechanisms to the EU’s climate strategy. They aim to reduce greenhouse gas emissions and prevent carbon leakage, where production shifts to regions with looser emission constraints. Established in 2005, the EU ETS operates on a cap-and-trade principle. It raises production costs for EU industries compared to international markets, leading to losses in market shares. To address this, the CBAM places a tax on imports based on their carbon content. This aligns foreign producers with EU carbon pricing and targets emission-intensive goods primarily in the manufacturing sector. This approach discourages the relocation of carbon-intensive production. It also incentivizes non-EU countries to adopt similar measures. By integrating CBAM with the EU ETS, the EU aims to maintain industrial competitiveness and promote global decarbonization efforts.

This thesis quantitatively assesses the impact of the CBAM on the price competitiveness of emission-intensive and trade-exposed industries in Germany. Germany is the EU's largest economy and a key player in manufacturing and exports. By developing a Leontief input-output price model and using data from Eurostat's FIGARO database, the study examines the effects of CBAM on production costs, export competitiveness, and domestic market competitiveness. The main research question is: "To what extent does the CBAM change the price competitiveness of carbon-intensive industries in Germany?" This price-centric analysis provides industry stakeholders with insights into the immediate cost impacts of CBAM on their sectors.

The analysis reveals that Germany’s manufacturing sector sees a minimal average production cost increase of 0.8% with the CBAM. Sub-sectors like basic metals, non-metallic mineral products, and refined petroleum face cost increases up to 3%. The CBAM effectively mitigates domestic market competitiveness deterioration by raising import prices from non-EU countries more than domestic production costs. However, it negatively impacts export competitiveness, necessitating additional measures such as subsidies, tax credits, export rebates, and “contracts for difference.” The CBAM also presents opportunities for revenue recycling into green infrastructure and technologies. The study underscores the need for supportive export-oriented policies to maintain Germany’s industrial competitiveness under stringent climate regulations. Despite some export market losses, gains in domestic markets are more pronounced. This understanding of the CBAM's economic implications is important for the EU’s climate strategy and economic integration. The results of this thesis have several limitations due to modelling simplifications. These include assumptions of no behavioral changes by companies, fixed production inputs, and linear technology, which may not reflect real-world adaptability and substitution capabilities. Additionally, the model assumes non-EU countries face the full CBAM tax without considering their emissions trading systems or free allowances within the EU, potentially overestimating cost impacts.

This thesis contributes to the existing literature by offering a targeted, sector-specific analysis of the CBAM's impact on Germany's manufacturing economy. Prior studies have largely overlooked this, focusing instead on aggregated country-level impacts or external trade partners. Emphasizing price impacts and using a detailed input-output model, the research provides actionable insights for policymakers to refine the CBAM and support vulnerable sectors. It adds to the policy debate on balancing environmental goals and industrial decarbonization by identifying the most affected manufacturing sub-sectors. The comparative analysis between Germany and the EU highlights the differential impacts of the CBAM and shows Germany's specific vulnerabilities and strengths, such as the mitigating effect of high value-added in the chemical industry. The study also demonstrates the integrated effects of the CBAM and the EU ETS, providing insights into their interplay. Using the novel FIGARO database, the research sets a precedent for future studies to use similar data sources. Finally, it offers policy design considerations to mitigate negative CBAM impacts, providing a scientific foundation for policy adjustments.

Future work should focus on sector-specific studies in carbon-intensive industries like steel, cement, aluminum, and chemicals. These studies should explore the sectoral impacts of CBAM and consider revenue recycling models. Research should also integrate varying substitution rates and price elasticities in input-output models, using detailed data collection on compliance costs to improve accuracy. Combining input-output with agent-based modeling can provide further insights. Improved data-sharing practices between the EU and trade partners are essential. Standardized protocols ensuring consistency and reliability, potentially via international data spaces, will lead to more precise insights.

Reducing the environmental impact of household consumption is widely recognised to be essential for achieving sustainable societies. Designing targeted policies requires detailed data on which households contribute how much to environmental footprints.
While many studies look into average environmental footprints of households, assessments across household income remain limited and focused on carbon, energy or material footprints. Furthermore, the existing literature is dominated by Input-Output Analysis despite increasing data availability making it possible to use process-based Life Cycle Assessment (pLCA) as well, with the latter offering potentially much higher levels of detail.
This study addresses this gap by estimating the environmental footprints from the consumption of household goods, appliances and food for 200,000 European households from 24 European countries and the European Union (EU) using a pLCA approach. To do so, pLCAs for the EU covering 16 impact categories are mapped to expenditure data from the EU Household Budget Survey (HBS).
The research question is: How are the environmental footprints from the consumption of household goods, appliances and food distributed across household income groups in the EU?
The study focused on carbon, water use, land use and resource use (minerals and metals) footprints, which are considered to be headline indicators. The results show that the carbon, water use and land use footprints at EU-level (without Austria, Italy and Germany), are dominated by the consumption of food and therefore largely independent from household income. The resource use footprint is dominated by appliances. The 10% households with the highest income have, on average, 2.8 times the carbon footprint, 6.4 times the water use footprint, 3.4 times the land use footprint and about 8.2 times the resource footprint of households belonging to the lowest income decile. Income inequality (with about 28.6 times the mean household income for top decile vs bottom decile households at EU-level) is much larger. At the same time, the range of household environmental footprints is large in all impact categories due to factors other than household income. The difference to existing literature, which generally finds a stronger connection between income and impacts, particularly for the carbon footprint, mainly stems from the exclusion of mobility and housing.
The study furthermore discusses the significant limitations of HBS data and outlines what
improvements are required to get more robust, comprehensive and detailed estimates of household environmental footprints. Ideally, future HBS would record physical quantities and/or prices along with the expenditures, which would make it possible to consider product quality differences. Using digital tools might make it possible to record consumption all year round or even for multiple years and thus mitigate the bias from infrequent purchases.
Future research should include services, housing and mobility and use regionalised impact data and find mitigation strategies for infrequent purchases and the product quality problem. The pLCA approach should be upheld due to the granularity it offers, for example for the assessment of the distribution of EFs under scenarios.

The transition to renewable energy sources is a key strategy in mitigating climate change, underscored by international agreements like the Paris Agreement and the European Union’s target of sourcing 42.5% of its energy from renewables by 2030. However, integrating Variable Renewable Energy (VRE) sources, such as solar and wind, presents significant challenges for grid stability and balancing costs.

This study empirically investigates the impact of increasing Battery Energy Storage Systems (BESS) capacity on European balancing market prices. Using time-series data from five European countries between 2016 and 2019, multivariate regression models are applied to analyze the effects on three types of balancing services: Frequency Containment Reserve (FCR), Automatic Frequency Restoration Reserves (aFRR), and Manual Frequency Restoration Reserves (mFRR).

The results indicate that increased BESS capacity significantly reduces balancing prices, particularly in the FCR and aFRR markets. This research extends the findings from previous studies in Australia to the European context, highlighting the potential of BESS to offer cost-effective, CO2-neutral solutions for grid stability.

These findings have important implications for Transmission System Operators (TSOs), policymakers, and stakeholders in the energy market, suggesting that specific policy and strategic investments in BESS could enhance the cost-efficiency and sustainability of future electricity systems.

Digisation and the continuous advancement of Artificial Intelligence (AI) incites projections about automation altering occupations over many sectors and countries. With increasing availability of digital infrastructure, many parts of emerging economies will open their doors and become subject to digitisation, robots and AI simultaneously (Messina 2016, Das and Hilgenstock 2018), which for advanced economies have appeared slower, with less established power structures and at different times. With high uncertainty around and little literature on the subject, the societal implications of automation in emerging economies are ambiguous, with potentials of automation boosting economies and leveling the playing field of development one one hand, but also leading to exploitative dynamics, in which companies with intellectual property rights and know-how on automation technologies inflict harm on labour markets, economies and thus many societal layers of emerging economies. While a multitude of indicators on job transformation through digitisation, robots or AI and their respective implications on employment emerged and were discussed within the literature for advanced economies, lack of abundant data causes ongoing uncertainty for emerging economies.

This thesis addresses the scientific gap of who is at risk of automation in emerging economies. The literature review entails an overview on the field of labour automation, the social effects of automation, and the (projected) case for emerging economies. The quantitative analysis entails the calculation of two automation indicators, Routine Task Intensity for automation through digitisation and robots, and Suitability for Machine Learning (SML) for automation through AI for emerging economies. SML and RTI are compared among occupations and socio-demographic groups as well as their relationship towards each other. To identify patterns to susceptible socio-demographic groups within the labour markets of emerging economies, a cluster analysis is performed on the most pertinent and yet orthogonal demographic parameters. The attained insights on socio-demographic clusters and their susceptibility towards automation are discussed to develop are broader picture on how automation affects emerging economies in order to educate policymakers and aid in rethinking policy approaches in response to changes in skill demands.

This thesis finds ground for concern that emerging economies will be subject to a not just broader but also likely more sudden wave of digital automation than advanced economies, potentially magnified through historic power imbalances and global market dynamics. Our results demonstrate that also for the task compositions of emerging economies, it can be expected that with the advent of AI a substantively larger share of tasks, occupations and socio-demographic groups is susceptible to automation as compared to automation through digitisation and robots. Workers with low and high education show higher automation susceptibilities than mid-educated workers, although through different technologies. The finding that also high-educated workers are susceptible motivates the conclusion that education systems might see their traditional societal role of enablers of social mobility endangered. Finally, this thesis finds that women are systematically more susceptible to AI-enabled automation and the observation that men are more susceptible to automation through digitisation and robots in advanced economies does not hold true for emerging economies. This thesis concludes by calling for particular consideration of emerging economies in the field of automation in labour markets, the merging of more granular data for emerging economies and an institutional model of automation.

The Netherlands is subject to labour scarcity across a variety of industries. While labour scarcity has occurred in the past, only now the consequences of higher educational attainment have become prevalent in the Netherlands. Due to a shortage of vocational workers and a population that is getting older, circumstances ask for adequate policy-making. Although policies like increasing the formal retirement age are put in place to negate labour scarcity, it remains difficult to understand the long-term outcomes of policy implications made in the present.

Understanding this long-term behaviour of the dutch labour system is deemed useful to develop strategies that result in a well-functioning labour market. Since the labour market is intertwined with other systems (e.g. population, educational system and the economy) and is subject to uncertain future developments it results in increased complexity of understanding long-term dynamics. This research aims to develop a better understanding of the long-term behaviour of these systems in deeply uncertain futures.

The labour market is constantly subject to economic, socio-demographic and technological developments. As both a policymaker and employer, the Dutch government must respond to these developments. As a policymaker to promote the functioning of the labour market, and as an employer to attract sufficient qualified personnel to guarantee the provision of public services to Dutch society. Both goals are under considerable pressure due to an unprecedentedly tight labour market, leading to labour shortages in parts of the public sector. These shortages have far-reaching consequences for the functioning of the Dutch labour market and the ability to provide public services, such as the provision of care and the renewal of crucial IT systems.

The Dutch government is pursuing a labour market policy to combat these developments and mitigate negative effects. The Dutch public wage policy, determining an adequate level of public sector wages relative to private sector wages, constitutes an important aspect. The Dutch public wage policy intends to offer competitive wages in the public sector to attract sufficiently qualified personnel to provide public services. The government has the reference model in place for this, but there is limited knowledge of to what extent the reference model leads to competitive wages in reality. This study addresses this knowledge gap and, as the first quantitative evaluation of the Dutch reference model, looks at the research question:

How does the Dutch public wage policy translate into public-private wage differentials and sectoral shifts?

An answer to this research question is sought by means of empirical econometric analysis, coupled with a thorough theoretical foundation. Oaxaca-Blinder decomposition methods, adapted to the specifics of the Dutch labour market, have been applied to analyse the public-private wage differentials to assess the competitiveness of public sector wages. Correlation analysis is applied to analyse the relationship between these wage gap estimates and sectoral job mobility to assess the influence of public-private wage differentials on the ability to attract qualified personnel in the public sector. Together, this forms an evaluation of the reference model, and thus the current public wage policy, addressing the questions of whether public sector wages are competitive with private sector wages and whether this (non)competitiveness can be a cause of shortages in the public sector.

The findings indicate that the current public wage policy does not lead to competitive public wages. While wages are reasonably competitive for the weighted average public servant, this is not the case when looking at specific personal human capital characteristics. Depending on one's capacities, one earns relatively more or less in the public sector than in the private sector. The reference model is too generic to be competitive for individuals and is only reasonably competitive when looking at the average. Application of compensation policies, as was done in 2015 and 2016, leads to a serious deterioration of public sector competitiveness. It is also established that these considerable wage differences can be a cause of shortages of qualified personnel in the public sector, including healthcare and ICT personnel.

The Dutch government should ask itself whether it wants to offer these non-competitive wages. There are egalitarian reasons for this approach, but there is a risk that there will be shortages of qualified personnel in the public sector. To respond to this, the reference model should be examined more closely, other options for attracting sufficient personnel in the public sector should be investigated, and options for increasing productivity in the public sector should be investigated. Academic points for future research are to apply other methods to the rich data set used in this study and to conduct further research into the causality between wages and sectoral job mobility, also in relation to other Public Service Motivation (PSM) and intrinsic motives.

The research conducts an analysis of the Dutch day-ahead electricity market prices spanning from 2015 to 2022, examining the relationship between increasing RE penetration and day-ahead electricity price fluctuations. The electricity price fluctuations over this time period are reviewed for patterns and recalculated for inflation corrections that contribute to a surge in electricity prices post-August 2021.

This price volatility is further researched by three energy market phenomena. A part of these price fluctuations can be explained by the presence of the Duck Curve phenomenon in the Dutch market, highlighting challenges associated with limited RE production time and energy demand. The Duck Curve, which illustrates the net load over a 24-hour period, reveals a decrease in fossil energy production during daylight hours and a peak in production during the night. When compared to California, this Dutch Duck Curve is more stable with the incorporation of wind energy, signifying a more reliable and consistent energy supply throughout the day.

Moreover, the study delves into another energy market phenomenon called the merit order effect, where the increased penetration of RE with low marginal cost leads to declining day-ahead electricity prices. The phenomenon is researched through the implementation of an OLS regression analysis. By plotting the results in individual months and years the merit order effect is visualized. The results are showcasing increased volatility, particularly in the latter part of the analyzed period (2020-2022). Fluctuations resembling a Duck Curve are observed, emphasizing the impact of RE implementation on price decline during periods of abundant RE supply.

Additionally, the cannibalization effect phenomenon is addressed where the growing penetration of RE undermines their own economic value. The Unit Revenue and Value Factor are calculated to aid the analysis. Followed up by the Prais-Winsten method to quantify this cannibalization effect. The results are revealing negative correlations between solar and wind shares and Unit Revenue and electricity prices. Thus, as RE penetration increases, electricity prices decrease, underscoring the cannibalization effect's influence on price fluctuations.

In conclusion, this study offers insights into the dynamic transformation of the Dutch electricity market. The identification of key phenomena such as the Duck Curve, the merit order effect, and the cannibalization effect provides empirical evidence of the market's evolution. These findings underscore the critical need for strategic management, targeted interventions, and innovative solutions. It is imperative to navigate these changes effectively to facilitate the ongoing expansion of renewable energy technologies while upholding the stability and competitiveness of the electricity market.

One of the main strategies for reducing energy demand—and thereby carbon emissions— is increasing energy efficiency. In the Netherlands, energy efficiency policy and energy reduction targets in the residential sector are based on the theoretical energy consumption of buildings, which often over- or underestimates the measured energy consumption. The difference between the actual energy consumption and the calculated energy consumption of a building is referred to as the energy performance gap (EPG). As a consequence of this discrepancy, the estimated energy savings are inaccurate and the energy-saving targets are unattainable. Narrowing the EPG is necessary to develop more accurate estimates of energy savings and realistic targets, and to improve the effectiveness of energy reduction policies and campaigns. Most studies on the EPG have focused predominantly on data from the social rental housing sector, failing to represent the national distribution of home ownership type. At the same time, homeowners and tenants have been shown to behave differently regarding energy consumption.
This thesis investigates the effect of home ownership on the actual natural gas consumption and the EPG by descriptive statistics, correlation analysis, and multiple linear regression on a representative sample of the Dutch housing stock. The multiple regression analysis controls for building and occupant characteristics that are expected to influence the actual gas consumption and the EPG, in order to measure the ceteris paribus effect of ownership type on the actual gas consumption and the EPG.
The results show that ownership type does not have a practically significant effect on actual gas consumption or the EPG, while controlling for building and household characteristics. However, without controlling for these factors, there is a moderate positive correlation between home ownership and actual gas consumption, and a weak positive correlation between home ownership and the magnitude of overpredictions. This suggests that observed differences in gas consumption or EPG between ownership groups may be explained by building and household characteristics, rather than by potential behavioral differences. Specifically, the positive correlation between home ownership and actual gas consumption can be explained by the larger floor area, type of buildings, higher income, and larger household size. The positive correlation between home ownership and the size of overpredictions is explained by type of building and larger floor area. Thus, there are no major differences in energy consumption behavior between homeowners and tenants that cause large differences in their actual gas consumption or EPG. Nevertheless, the distinction of ownership type may still be of practical use to policymakers. Targeting homeowners could be an efficient way to promote energy-saving measures in the largest and highest energy-consuming dwellings.

By design, excise duties are primarily a tax which serves to generate revenue for the central government. However, when they are placed on fuels they can, from a consumer point of view, be regarded as similar to carbon taxes. Carbon pricing, and carbon taxes in particular, are often championed by experts in relation to reducing carbon emissions. Based on these aforementioned notions, this research set out to investigate the role excise duties on fuel can play as an intervention employed by policy makers looking to reduce CO2 emissions. With this goal in mind, a Dutch case study is performed. This case study utilizes the synthetic control method to empirically investigate if the increase in Dutch excise duties on gasoline can be causally linked to CO2 emission reductions from road transport. Ultimately, the research found that an increase in the excise duty on gasoline can result in a significant decrease in per capita emissions. These findings resonate with the literature, and point towards how excise duties can play a part in strategically designed environmental policies. From a policy maker’s perspective, this means that in combination with complementary environmental policies such as technology mandates, raising the price of excise duties on fuel could prove to significantly contribute to emissions reduction goals.

Climate change has increased the interest around energy intensity to many researchers. Understanding the drivers of energy intensity is essential to produce a fitting policy. While structural changes have been acknowledged as drivers of economy-wide energy intensity, it has not been recognised as an influential factor on industry-level energy intensity because there is simply not enough data to do so. To generate a more reliable understanding of the mechanism that drives industries’ energy intensity, I explore the role of international trade on inter-country differences in the energy intensity of industries. The approach in this thesis shows with multiple panel data regressions how changes in trade specialisation in products explain inter-country differences in energy intensity of industries. The results estimate
that an industry’s energy intensity increases when it develops a comparative advantage in products whose production requires lots of energy, ceteris paribus, and decreases when it develops a comparative advantage in products whose production requires little energy. Since the effect of trade specialisation in one product goes on behalf of the effect of trade-specialisation in other products, there is a strong suspicion that there are changing compositions of industries. For policy changes, these results can provide insights into how a centralised approach in trade agreements and a decentralised approach in industry policies can tackle the industry’s energy-intensity problems. In future research, it might
be possible to accurately predict the composition effects by explaining the changes in the energy intensity of products with trade specialisation in products.

This dissertation aims to contribute towards the research on just transitions by providing insight into the extent of the employment impacts caused by a potential energy transition in South Africa. South Africa is in the process of transforming its electricity mix. The majority of electricity is currently sourced from coal-fired power plants. The transition maybe towards an electricity mix that includes more renewable sources of electricity generation, particularly from wind and solar technologies. The degree to which this transition will create and displace jobs is presently unclear. Furthermore, the extent to which these new jobs will be demanded locally or abroad and the implications that this transition will have on decent work is uncertain.The main research question posed at the outset of this dissertation was: What are the possible net employment effects of South Africa’s transition from coal to wind and solar forms of electricity generation? A quantitative method was employed to address this question: an Input-Output Analysis. The I-JEDI input-output model was used, updated with more recent data for South Africa that has more industrial detail. Additionally, a coal displacement vector was created to calculate the jobs displaced due to lack of demand for coal-fired power plants. Hence, the net employment effects were calculated. This quantitative method was supplemented with findings from a series of semi-structured interviews and a workshop with union members to ground the results in some of the realities of South Africa’s socio-political landscape. Two exploratory scenarios were createdthat were simulated in the model, each with their own narrative about South Africa’s future electricity prospects: Integrated Resource Plan (IRP) 2019 scenario and the Ambitious Solar and Wind (ASW) scenario. The IRP 2019 scenario was aligned with the recent government plans to transform the electricity mix. The ASW scenario included higher levels of local manufacturing of solar and wind technologies than currently employed. The results of this research are exploratory, aiming to provide insight into the boundary of what is possible. The main findings suggest that solar and wind may employ significantly more people than currently employed in the coal-fired power plant supply chain considering both operations and maintenance (O&M) and construction activities. O&M jobs numbers for coal-fired power plants are larger than wind and solar under the IRP2019 scenario but are comparable for the ASW scenario. While the results from the model showed promising prospects for employment, it was revealed through the stakeholder interactions and literature that there are several challenges to achieving a just energy transition. In particular, there is the challenge of garnering the support of labour unions and ensuring decent work. It was found that the overall skill levels in terms of education for the different sources of electricity were similar, but there are many other skill considerations required for people to change vocation from the old jobs in coal to new jobs in solar and wind.

This research will calculate the extended LCOE of internal hydrogen production and stor- age in offshore wind turbines at remote locations and will be done in collaboration with Van Oord. Van Oord is a global offshore marine contractor which is focused on dredging, land infra-structure, offshore wind and oil gas infrastructure.

To test the competitiveness of this technology a test location is selected; Grenada in the Caribbean. Grenada is selected because of its suitable conditions: the island is remote, which makes energy supply from outside the island not feasible. The current energy is generated with diesel generators, whereby the diesel is transported from onshore. This makes the elec- tricity relatively expensive and creates excessive carbon emissions compared to sustainable energy systems. This high electricity price also makes renewable energies competitive in an early stage of development. Lastly, the location conditions are favourable for wind energy; high average wind speed, high capacity factor and water depths which are suitable for fixed bottom wind turbines.

To calculate the LCOE of this technology, first a base load calculation is made with the wind speed data from one year. The output of this calculation is the input for the technical com- ponent selection of the hydrogen system. The overproduction of the offshore wind turbine is used to produce hydrogen with an electrolyzer. For this process, fresh water is required, which is produced by a desalination system. The hydrogen is pressurized with a compressor and stored in a storage tank. If the wind turbine is not able to produce the required energy, the hydrogen is reconverted into electricity. All these components are located inside the foundation of the offshore wind turbine. The LCOE is calculated with the hydrogen com- ponent cost and the cost overview of the the wind turbine. This design is tested for the Grenada location, with various base load scenarios and number of wind turbine generat- ors (WTG’s). A sensitivity analysis on these input variables will show their impact on the LCOE and makes design optimization possible. The extended part of this LCOE analysis is the carbon emission decrease calculation between the current non-sustainable diesel gen- erated energy and the offshore internal hydrogen wind turbine system. This sustainable impact is converted into financial input values which shift the financial competitiveness of the technology.

With the base load and LCOE models the following results are obtained. The extended LCOE for an internal hydrogen production and storage system depends on many variables and therefore is given as a range. The LCOE of the Grenada scenarios ranges between †604.03-246.53/MWh. If electricity is supplemented with diesel generated power, at the current energy price, the LCOE can drop to †230.09/MWh. For a single structure with gen- eralized system design, Grenada location input, without the use of overproduction and a base load of 1.5 MW, a LCOE of †501.32/MWh is calculated. If this energy is used, this LCOE will drop to †390.46/MWh. With the addition of the hydrogen system, the security of supply of a traditional wind turbine is increased from 68% to 76%. Depending on the configuration and number of WTG the security of supply can increase to 100% for low base loads. The extended analysis of this LCOE calculation is the sustainable impact of the tech- nology. According to the calculations, the assumed carbon emissions can be reduced up to 96% for a wind turbine system supplemented with diesel energy and up to 99% if the energy production is only generated with wind turbines. If carbon certificates need to be bought, the competitive financial break-even point of this technology will shift. For Grenada, the price of these certificates ranges between †42.03/kg tonnes of CO2 up to †489.86, depending on the selected base load and configuration.

From the sensitivity analysis the influence of the parameters on the lCOE are obtained; an increase in base load, Number of WTG’s, power output or storage volume will de- crease the LCOE. However the LCOE will increase if the energy losses, discount rate, OPEX, CAPEX, decommissioning cost or growth rate are increased. Most of these variables are inter-depending, which requires iterations to decrease the LCOE.

Many stakeholders are involved in the roll-out of new electricity technology. For Grenada as remote location, the stakeholders are the electricity provider, electricity consumers, gov- ernment, investors and secondary stakeholders. The competitiveness of a technology is not only depending on the LCOE, even with a sustainable impact analysis, but also on the im- plications for these stakeholders. Examples of these additional factors are view pollution, political incentives and benefits from byproducts.

Due to the early stage of development, many assumptions and simplifications have been made. These assumptions can be divided in four categories: technical, financial, location specific and stakeholders. Examples are; the foundation is assumed to be feasible for this technology, the carbon emissions are only based on the direct diesel emissions and the car- bon emissions of the wind turbine and the base load calculation are based only on a data set of 2020. Therefore future research is recommended for detailed analysis on this technology.

Global warming is one of the most significant concerns for human beings today in the 21st century. Under this circumstance, countries and regions around the world are taking action to reduce greenhouse gas emissions. China, as the world's largest emitter, plays an important role in reducing carbon emissions worldwide. This paper aims to find the changes in China's carbon emissions from 2000-2014 and find the main driving forces for it which can provide suggestions for China's further carbon reduction strategies.

Maharashtra, the second largest state in India in terms of population and area, is a fast-developing economy and meeting its electricity demand is crucial for its economic growth. Currently, power production is causing severe environmental and security issues mainly because of the reliance on coal plants in the state. In order to meet SDG 7 “affordable and clean energy” proposed by UN, a government think tank called NITI Aayog helps set polices and targets for sustainable development of electricity production in states of India. However, the stakeholders are hindered to address the problems of sustainability dimensions due to a significant knowledge gap causing discrepancies in power policies. There is a need for a comprehensive approach which involves life cycle thinking and integration of the sustainability dimensions. Hence, this study adopts an integrated approach of environmental and economic dimensions of power sector in Maharashtra for suggesting NITI Aayog on policies and framework which can be used in the expansion of electricity generation capacity. The framework followed in this study consisted of the four steps, indicator selection, environmental performance evaluation, economic performance evaluation, integration and policy implications. For this work, solar PV and nuclear technologies were selected and then the dimensions of technologies were integrated with respect to the predominant technology, coal. Firstly, in order select indicators which relevant to SDG 7 and our case study, a literature study was conducted based on several criteria. After the analysis on the studies selected, GWP and LCOE indicators for environmental and economic dimensions were selected. Furthermore, for evaluating the environmental performance of the technologies, LCA studies were used to quantify GHG emissions and to identify hotspots in life cycle stages. A methodology was followed to harmonise the published results of the selected studies to reflect the current status of Maharashtra conditions. The GHG emissions of solar PV was found to be 39 g CO2 eq/kWh of which 89% was contributed from manufacturing phase of the modules whereas GHG emissions of nuclear energy was estimated to be 12.5 CO2 eq/kWh in which 75% of the emissions is during HW production. Over time, solar PV emissions tend to decrease because of the improvements in the system efficiency while nuclear energy emissions tend to increase because of decreasing uranium ore grades. Thirdly, evaluation of economic performance of both technologies was carried out by using LCOE tool. DCF method was used to bring the future costs to NPV and after calculating LCOE, a sensitivity analysis was performed to know the influential parameters. Under the defined system boundaries, LCOE of solar PV was estimated to be 0.045 USD/kWh in which 85% of the costs are capital costs. whereas LCOE of nuclear energy was calculated to be 0.055 USD/kWh in which capital costs and O&M costs account to 50 and 37% respectively. Over time, the LCOE of solar energy tends to decrease with improvements in technology while LCOE of nuclear tend to increase because of the increasing safety standards and inflation. Lastly, an MCDA method, weighted sum approach was chosen for integration of two aspects as it provides platform for stakeholder participation and provides a transparent, inclusive and organised framework. Three scenarios were considered in which even extreme weights were considered for calculation of sustainability scores and the overall ranking of technologies remained same in the given scenarios. Clearly, solar energy is the winner in the range of the assumed extreme weights and is followed by nuclear and coal energy. Based on the outcomes of LCA and MCDA of this study, policy implications were discussed. On economic front, electricity bundling of solar and nuclear energy is recommended to reduce the overall cost and to solve the issue of intermittency of solar energy. On the environmental front, GHG hotspot phases in both solar PV and nuclear energy were identified and policy recommendations were discussed. Overall, policies should be aimed at lowering or phasing out the fossil fuels and at providing enabling environment for increasing the low carbon technologies into the energy mix. As far as LCA and MCDA framework in this research is concerned, the sustainability scores of the technologies help ranking the technologies. More than ranking the technologies, the deliberative process makes stakeholders come together on a platform to formulate problem, discuss trade-offs and come up with unique solutions to reduce the impacts. NITI Aayog can use the framework not only for the case of Maharashtra, but also for different states. The framework allows to arrive at a customised solution according to the preferences of all stakeholders while still be able to measure and compare the improvements in the sustainability aspect.