Voice messages are an increasingly well-known method of communication, accounting for more than 200 million messages a day. Sending audio messages requires a user to invest lesser effort compared to texting while enhancing the meaning of the message by adding an emotional context (e.g., irony). Unfortunately, we suspect that voice messages might provide much more information than intended. In fact, speech audio waves are both directly recorded by the microphone, as well as propagated into the environment and possibly reflected back to the microphone. Reflected waves along with ambient noise are also recorded by the microphone and sent as part of the voice message. In this thesis, we propose a novel attack for inferring detailed information about user location (e.g., a specific room) leveraging a simple WhatsApp voice message. We demonstrated our attack considering 7,200 voice messages from 15 different users and four environments (i.e., three bedrooms and a terrace). We considered three realistic attack scenarios depending on previous knowledge of the attacker about the victim and the environment. Our thorough experimental results demonstrate the feasibility and efficacy of our proposed attack. We can infer the location of the user among a pool of four known environments with 85% accuracy. Moreover, our approach reaches an average accuracy of 93% in discerning between two rooms of similar size and furniture (i.e., two bedrooms), and an accuracy of up to 99% in classifying indoor and outdoor environments.

Celsius or 1085 GtC. This thesis explores this significance and points toward prioritizing ecosystem conservation as a highly effective strategy for mitigating climate change. Initiatives such as carbon offsetting, performed by organizations like ‘8 billion trees’ and ‘cool earth’, could provide feasible solutions to enhance carbon sequestration. The EU's Nature Restoration Law is a step in the right direction, advocating for the conservation of existing ecosystems and restoring deteriorated systems, specifically increasing organic stocks in forests and restoring and rewetting drained peatlands of agricultural use and peat extraction sites seem to be compelling aspects in terms of improving the carbon system proposed in this law. I encourage the exploration of similar policy changes globally, underscoring the importance of taking immediate action and ensuring no further deterioration of existing terrestrial carbon systems. Following the importance of terrestrial carbon storage, the debate remains about who should sacrifice their land. This research has displayed a high sensitivity in land allocation for terrestrial carbon storage when comparing ethical perspectives of justness, also defined as climate justice principles. This sensitivity can be seen as normative uncertainties and represent potential tensions in developing a coherent global policy. In my research, certain countries are more sensitive to these normative uncertainties than others. The Netherlands, Pakistan, China, and Yemen are highly sensitive to terrestrial carbon storage obligations if islands and smaller states are not considered. In the case of the Netherlands, this sensitivity is caused by a high GDP and, therefore, ability-to-pay. Still, when following efficiency prioritized, Dutch agricultural land should be kept for its high agricultural productivity. China is expected to reach a high level of domestic food security. Therefore, they would have to sacrifice their land for terrestrial carbon storage according to a different interpretation of ability-to-pay. Also, the efficiency prioritized principle for China would be ideal because, with their economic growth and innovation, they are expected to reach a high level of agricultural productivity. In the case of Yemen, it is different, Yemen has low agricultural productivity, and efficiency-prioritized will lead to them converting agricultural land to forests. The best interpretation of ability-to-pay for Yemen is the available land that could be converted for terrestrial carbon storage. In my analysis, I showed proof of principle to include these normative uncertainties in policy analysis to explore areas of consensus. Through this approach, I found countries that should convert or conserve their land for terrestrial carbon storage and countries that arguably could still replace their forests and wetlands for other land covers such as urban- and agricultural land. I propose that climate justice principles should be included as uncertainties in policy-analysis, thus arguing for – additional – simplified models to allow for easy implementation of these uncertainties in the analysis. To come to these findings, I performed an extensive analysis of the global food-, land cover-, and carbon system, simulating a wide range of scenarios up to the year 2100. The model I have developed operates at a national-detail level and includes 173 countries worldwide. The model considers several factors for each country, such as socioeconomic development, current land cover, agricultural productivity, and food demand. The national systems interact globally to represent global systems such as food change and the carbon cycle. I tested a wide range of distribution methods on this system for terrestrial carbon storage burden, represented by five policy levers. Additionally, I considered 21 uncertainties in the model and performed 10.000 experiments to ensure the robustness of the proposed policies. In the last phase of the analysis, I created world maps that display the land countries should convert for terrestrial carbon storage according to climate justice principles and the sensitivity per country. These maps allow for an easy understanding and interpretation of the climate justice principles. I started the research by exploring frequently used climate change policy evaluation in ethics. Ethics literature discusses two main social justice types: distributive and procedural justice. In this study, I focus on distributive justice for its applicability in modelling solutions. I utilize pre-defined climate justice principles and introduce a new principle – efficiency prioritized, based on the utilitarian principle. The ‘principle efficiency prioritized’ allocates agricultural land to countries with the highest agricultural productivity and terrestrial carbon storage burden to the less-productive countries. Selected principles are Ability to Pay (based on available land for terrestrial carbon storage, domestic food supply, and GDP per capita), You-Broke-It-You-Fix-It (based on historical emissions), and Efficiency Prioritized, with different interpretation methods considered for Ability to Pay. I modeled the system using Vensim, a System Dynamics modeling software. System Dynamics is a quantitative modeling formalism suitable for dealing with complex systems, allowing for exploring relations between system structure and behavior. It helps to understand the complex land use, land-use change, and forestry, food, and carbon system. It also enables the incorporation of parametric and structural uncertainties, testing a wide range of scenarios in systems with deep uncertainty. The model consists of several sub-systems, leveraging existing models from prior research by Auping (2018), and takes data from sources such as the World Bank, FAOSTAT, OECD, and IPCC as input. To find robust policies in the face of deep uncertainty, I adopted an Exploratory Modelling and Analysis (EMA) approach using the open-source EMA workbench for Python. This approach involves performing a broad range of computational experiments, analyzing the results, and identifying robust policies based on these findings. EMA workbench has a special connection built-in to run experiments with Vensim efficiently. It includes built-in functions for sampling, such as Latin Hypercube Sampling, and scenario discovery, such as PRIM, used in my analysis to find the scenarios of interest. I visualized the results with Basemap, NumPy, Pandas, and the EMA workbench, among other tools. Creating maps allows for a simple data representation, enhancing understanding and communicating the findings. This approach enables a thorough exploration of climate justice principles, modeling, and experimentation, providing valuable insights for decision-makers facing complex and uncertain climate change challenges. To summarize, I applied System Dynamics modeling and scenario discovery to explore the global food and carbon system. I proved the importance of terrestrial carbon storage, especially conserving existing forests and wetlands. Terrestrial carbon storage policy also faces policy challenges in implementation, mainly the distribution of the terrestrial carbon storage burden. Because land for terrestrial carbon storage goes at the cost of agricultural and urban land that could provide economic prosperity and food security, there is a trade-off between different Sustainable Development Goals. Several distribution conventions have been evaluated following climate justice principles to find overlapping policies between the principles. I found a significant difference in the distribution of terrestrial carbon storage obligations between the principles, displaying potential complexities and tensions in policy-making. I also found consensus between the principles, defined as non-discriminatory policies. Policy-makers should start with non-discriminatory policies to ensure swift implementation. I recommend that other policy modelers also include ethical perspectives as uncertainties in their models to find non-discriminatory policies and, if necessary, develop simplified models to enable this.

Integrated Assessment Models (IAMs) are widely used tools for estimating climate change's impacts on the socio-economic system. However, IAMs possess a limited ability to represent the impact on vulnerable populations. Influential IAMs use highly aggregated outcomes masking significant relative differences in impacts across heterogeneous populations and time. Therefore IAM studies systematically undervalue the need for faster CO2 emission reductions to prevent substantial climate impacts on low-income groups. Previous literature has proposed alternative distributive principles to use in IAMs to overcome inequity problems in current abatement pathways. Due to the heterogeneous models, scenario assumptions, and principles used, comparison between studies has been difficult. Therefore the literature lacks an overarching comparative study of the performance of various distributive principles. Furthermore, deep uncertainty present within climate change is often not considered because most studies focus on single-scenario optimality. Therefore, current abatement pathways contribute to maintaining current economic inequalities and possess exposure to uncertainty. This thesis applies alternative principles to the RICE model through a MORDM method. Alternative distributive principles are used to generate more equitable abatement strategies using a MOEA. This research analyses four main principles: the Prioritarian, Sufficitarian, Egalitarian, and the Utilitarian principle. Alternative principles generate significantly different abatement trajectories than the Nordhaus optimal policy. Sufficitarian policies have the most stringent abatement targets focusing on reaching net-zero emissions by 2065. Prioritarian strategies aim at 2065-2105. Utilitarian strategies focus on reaching net-zero emissions around 2135. Lastly, Egalitarian policies enforce slow climate abatement. Abatement strategies have been stress-tested through an uncertainty analysis based on the SPP-framework and exposure to long-term climate uncertainty. Sufficitarian strategies possess high robustness to climate extremes and high average performance across all scenarios. Prioritarian strategies possess high average robustness but lower regret-based robustness. This research shows that alternative principles can successfully generate well-performing abatement pathways in the RICE model that outperform Utilitarian-based policies. The Sufficitarian principle's capacity to produce equitable and economically optimal strategies has been undervalued in previous research. Lastly, this thesis emphasizes that stronger climate action is needed to prevent possible disastrous worldwide impacts on low-income groups. Global emission reductions should adhere to a global warming of 1.5 degrees set out by the IPCC, which is consistent with the best-performing pathways found in this research. A first step would be to reduce existing economic inequalities to overcome exposure to climate extremes for lower-income groups.

As part of the EU Green Deal, the European Commission (EC) has announced a new renewable energy policy instrument that is to be installed in addition to the Emission Trading Scheme (ETS) of the EU: the Carbon Border Adjustment Mechanism (CBAM). CBAM obligates companies, from non-EU countries who want to import products from the selected CBAM sectors – Iron & Steel, Aluminium, Cement, Fertilizers and Electricity – into the EU, to pay a certain tariff for the carbon content of that product. Initially, CBAM covers only imports and the carbon emitted during the actual production of the products. CBAM has four objectives: (i) preventing carbon leakage; (ii) protecting EU industries against reduced competitiveness; (iii) incentivising non-EU trade partners to adopt measures comparable to the EU’s; and (iv) yielding revenue to reuse in accelerating decarbonisation of the energy system. Using interviews and a literature study, a case study was conducted on the dynamics of the trade flows of aluminium between Germany and China. Given these dynamics, six themes were studied to assess the consequences of CBAM: (i) compatibility of the policy instrument; (ii) administrative work needed; (iii) stimulation of decarbonisation in non-EU countries; (iv) competitive position of the German aluminium sector; (v) economic and geopolitical winners and losers; (vi) the three major risks of CBAM. This led to four factors that help in assessing the influence of CBAM and making CBAM more effective in achieving its objectives. These factors are tailor-made, manageability, export dependency and geographical closeness. Assessing the impact of CBAM on a specific sector from an economic and geopolitical perspective proved to be a highly complex process, where each factor is surrounded by a lot of uncertainty. The four factors can be used in further research on CBAM, as well as its future implementation.

Climate change is a topic being addressed by many countries worldwide. One of the most recent treaties is the Paris Agreement signed in 2015 by 196 parties, where they agree on a common goal: restrict global warming to be climate-neutral by 2050. Peru by being one of the signing parties that played a relevant role before and during the Paris Agreement discussions, submitted their Nationally Determined Contributions in 2016. Even when climate policies are implemented, their success is found to be related to society's acceptance of them. Moreover, citizens' support for policies is related to the alignment of such policies with their preferences, and research shows that citizens' preferences are based on their values. Additionally, it is known that there are balanced tensions between values, which are translated into trade-offs. Different participatory methods have been used for preference elicitation. In Peru, however, it has been found that the processes followed have an apparent entry barrier for individuals, therefore, other methods can be explored. A recently developed method is Participatory Value Evaluation, in which the participants are asked to make a selection among a set of alternatives while considering its effects and one or more restrictions. This method has been mainly applied in Europe, with some of them being focused on climate change subjects. Based on this the primary and secondary research questions are defined as: 1. How do Peruvian citizens trade-off climate change mitigation measures among the two most polluting sectors, from a set of policy alternatives? 2. How is Participatory Value Evaluation perceived by Peruvian citizens and how does it differ from Dutch citizens' perception? Based on the literature review done regarding the method, a Participatory Value Evaluation experiment is designed using the knowledge gathered from the research on the climate policy situation in Peru and its region. This includes a total of six policy measures part of the country's two most polluting sectors (i.e., Energy and Agriculture, Forestry and Other Land Use). The designed Participatory Value Evaluation is conducted between July 3rd and July 17th, 2023. It can be concluded that Peruvian citizens prefer policy alternatives that yield positive and direct environmental outcomes, especially when accompanied by minimal monetary expense. Citizens would trade off a higher cost for even a moderate reduction in harmful emissions, provided the health benefits are explicitly articulated. The inclination to make a trade-off between cost and emission reduction diminishes when health and environmental benefits are either not explicitly outlined or fail to reach a significant threshold. Finally, based on the preference patterns of the individuals, four distinct groups were identified. These are mainly dependent on their considerations of the effects each policy alternative may bring. These insights reinforce the importance individuals across different education levels, attribute to the environment, particularly regarding deforestation. Regarding the method, it can be concluded that Peruvian citizens had a positive and favourable experience when participating in the PVE for climate change mitigation. In a similar manner to the Dutch citizens, Peruvian individuals exhibited a positive perception of the method. In like manner, a majority of individuals believed it is a good method to involve citizens in governmental decisions regarding climate change policies. These insights seem to foster an inclination for continued engagement and provide their opinion on other subjects relevant to Peruvian society. Equally important, citizens would appreciate the real repercussions resulting from their input within the decision-making process.