• Energy Research
• Embargo close
• GB close
• NL close
• RU close

Modelling the economics of climate change is daunting. Many existing methodologies from social and physical sciences need to be deployed, and new modelling techniques and ideas still need to be developed. Existing bread-and-butter micro- and macroeconomic tools, such as the expected utility framework, market equilibrium concepts and representative agent assumptions, are far from adequate. Four key issues—along with several others—remain inadequately addressed by economic models of climate change, namely: (1) uncertainty, (2) aggregation, heterogeneity and distributional implications (3) technological change, and most of all, (4) realistic damage functions for the economic impact of the physical consequences of climate change. This paper assesses the main shortcomings of two generations of climate-energy-economic models and proposes that a new wave of models need to be developed to tackle these four challenges. This paper then examines two potential candidate approaches—dynamic stochastic general equilibrium (DSGE) models and agent-based models (ABM). The successful use of agent-based models in other areas, such as in modelling the financial system, housing markets and technological progress suggests its potential applicability to better modelling the economics of climate change.

Modelling the economics of climate change is daunting. Many existing methodologies from social and physical sciences need to be deployed, and new modelling techniques and ideas still need to be developed. Existing bread-and-butter micro- and macroeconomic tools, such as the expected utility framework, market equilibrium concepts and representative agent assumptions, are far from adequate. Four key issues—along with several others—remain inadequately addressed by economic models of climate change, namely: (1) uncertainty, (2) aggregation, heterogeneity and distributional implications (3) technological change, and most of all, (4) realistic damage functions for the economic impact of the physical consequences of climate change. This paper assesses the main shortcomings of two generations of climate-energy-economic models and proposes that a new wave of models need to be developed to tackle these four challenges. This paper then examines two potential candidate approaches—dynamic stochastic general equilibrium (DSGE) models and agent-based models (ABM). The successful use of agent-based models in other areas, such as in modelling the financial system, housing markets and technological progress suggests its potential applicability to better modelling the economics of climate change.

Extreme summer droughts are expected to occur more often in the future in NW Europe due to climate change. These droughts might accelerate the rate of peat oxidation in drained peat areas, with impacts on soil subsidence, GHG emission and water quality. This study aimed at providing more insight in the oxidation of deep peat layers that had not previously been exposed to air, the so-called secondary decomposition. We incubated two types of peat (eutrophic and oligotrophic), sampled from permanently anoxic peat layers from nature reserves and agricultural peat meadows. Peat samples were incubated for thirteen weeks under anoxic conditions, but were exposed to air for one to eight weeks. The production of CO2 and CH4 was quantified as a proxy for decomposition; concentrations of soluble nutrients and phenolic compounds were also measured. The results showed that oxygenation led to a steep increase in the rate of decomposition, indicated by higher carbon loss rates during and after oxygenation compared to non-oxygenated samples. Carbon loss rates increased more for eutrophic peat (agricultural area: 352%, nature reserve: 182%) than for oligotrophic peat (83% and 159% respectively). Most peat samples investigated showed higher post-oxygenation CO2 and/or CH4 production compared to the anoxic pre-oxygenation period. This indicates that oxygenation stimulates decomposition, even after anoxic conditions have returned. Contrary to the enzymic latch theory, no effects of oxygenation on the concentrations of soluble or condensed phenolic compounds were detected. Soluble nutrient concentrations did not change due to oxygenation either. Noteworthy is the occurrence of pyrite mineralization and associated acidification in eutrophic peat. Thus, low summer water levels, for example due to climate change, should be avoided in order to limit exceptionally high decomposition rates and associated problems such as increasing subsidence rates, greenhouse gas emission, sulfate release and acidification.

Extreme summer droughts are expected to occur more often in the future in NW Europe due to climate change. These droughts might accelerate the rate of peat oxidation in drained peat areas, with impacts on soil subsidence, GHG emission and water quality. This study aimed at providing more insight in the oxidation of deep peat layers that had not previously been exposed to air, the so-called secondary decomposition. We incubated two types of peat (eutrophic and oligotrophic), sampled from permanently anoxic peat layers from nature reserves and agricultural peat meadows. Peat samples were incubated for thirteen weeks under anoxic conditions, but were exposed to air for one to eight weeks. The production of CO2 and CH4 was quantified as a proxy for decomposition; concentrations of soluble nutrients and phenolic compounds were also measured. The results showed that oxygenation led to a steep increase in the rate of decomposition, indicated by higher carbon loss rates during and after oxygenation compared to non-oxygenated samples. Carbon loss rates increased more for eutrophic peat (agricultural area: 352%, nature reserve: 182%) than for oligotrophic peat (83% and 159% respectively). Most peat samples investigated showed higher post-oxygenation CO2 and/or CH4 production compared to the anoxic pre-oxygenation period. This indicates that oxygenation stimulates decomposition, even after anoxic conditions have returned. Contrary to the enzymic latch theory, no effects of oxygenation on the concentrations of soluble or condensed phenolic compounds were detected. Soluble nutrient concentrations did not change due to oxygenation either. Noteworthy is the occurrence of pyrite mineralization and associated acidification in eutrophic peat. Thus, low summer water levels, for example due to climate change, should be avoided in order to limit exceptionally high decomposition rates and associated problems such as increasing subsidence rates, greenhouse gas emission, sulfate release and acidification.

This paper proposes a novel online and self-learning algorithm to the identification of fuzzy neural networks, which not only learns the structure and parameters online but also learns the threshold parameters by itself and automatically. For structure learning, a self-constructing approach including adding neurons and merging highly similar fuzzy rules is proposed based on the criteria of the system error between actual and model output and the maximum firing strength of neurons. In order to achieve the efficient merging computing, a new calculation method of similarity degree between fuzzy rules is developed. Further and more importantly, the varying width of Gaussian membership functions can be learned by itself according to the underfitting and overfitting criteria. Similarly, different from the existing constant threshold of similarity degree for merging, the varying threshold of similarity degree can be self-learned according to the real-time accuracy of model. The proposed self-learning mechanism significantly improves the model accuracy and greatly enhances the easy usability. Several benchmark examples are implemented to illustrate the effectiveness and feasible of the proposed approach.

This paper proposes a novel online and self-learning algorithm to the identification of fuzzy neural networks, which not only learns the structure and parameters online but also learns the threshold parameters by itself and automatically. For structure learning, a self-constructing approach including adding neurons and merging highly similar fuzzy rules is proposed based on the criteria of the system error between actual and model output and the maximum firing strength of neurons. In order to achieve the efficient merging computing, a new calculation method of similarity degree between fuzzy rules is developed. Further and more importantly, the varying width of Gaussian membership functions can be learned by itself according to the underfitting and overfitting criteria. Similarly, different from the existing constant threshold of similarity degree for merging, the varying threshold of similarity degree can be self-learned according to the real-time accuracy of model. The proposed self-learning mechanism significantly improves the model accuracy and greatly enhances the easy usability. Several benchmark examples are implemented to illustrate the effectiveness and feasible of the proposed approach.

This thesis explores Germany’s *Energiewende* (‘energy transition’) and the role of citizen/community energy democracy initiatives at municipal and state levels in shaping developments that underpin this society-wide transformation - examining how grassroots agitation, in particular, has prompted meaningful action and policy change on the part of government at multiple levels. Using Berlin and Hamburg - Germany’s largest urban centers and leading city-states in the country’s federal system - as case studies, the research examines how citizens’ initiatives in both cities have campaigned for the remunicipalization of local energy networks to expedite, democratize, and incorporate social justice goals into each region’s Energiewende. Finally, this study pivots to consider the progress of the transition nationally in recent years, investigating how major events like the Covid-19 pandemic, watershed federal elections in 2021, war in Ukraine, and the resultant energy crisis have affected the evolution and direction of the Energiewende, and the important contributions of citizen/community energy initiatives to the national response here. The analytical framework draws on over 100 interviews conducted with relevant stakeholders and experts involved in the transition, representing different perspectives (e.g. on remunicipalization and the Energiewende itself) and diverse levels of government (e.g. from the district to federal level). This extensive body of original research is complemented by a wide array of other relevant source material that has likewise been consulted to inform this work and further evaluate the impacts of bottom-up energy democracy initiatives on the broader transition in Germany’s regions - and, by extension, nationally - as they continue to affect the pace and trajectory of the now-famous Energiewende. Ultimately, this study contributes an in-depth, ground-level analysis to the literature of key elements that have driven energy system change in Germany, shining a fresh light on the complex and interrelated nexus of sustained grassroots action, policy responses, and shifting sociopolitical realities that form the context in which the Energiewende has been (re)launched, reformed, and reimagined over the decades.

This thesis explores Germany’s *Energiewende* (‘energy transition’) and the role of citizen/community energy democracy initiatives at municipal and state levels in shaping developments that underpin this society-wide transformation - examining how grassroots agitation, in particular, has prompted meaningful action and policy change on the part of government at multiple levels. Using Berlin and Hamburg - Germany’s largest urban centers and leading city-states in the country’s federal system - as case studies, the research examines how citizens’ initiatives in both cities have campaigned for the remunicipalization of local energy networks to expedite, democratize, and incorporate social justice goals into each region’s Energiewende. Finally, this study pivots to consider the progress of the transition nationally in recent years, investigating how major events like the Covid-19 pandemic, watershed federal elections in 2021, war in Ukraine, and the resultant energy crisis have affected the evolution and direction of the Energiewende, and the important contributions of citizen/community energy initiatives to the national response here. The analytical framework draws on over 100 interviews conducted with relevant stakeholders and experts involved in the transition, representing different perspectives (e.g. on remunicipalization and the Energiewende itself) and diverse levels of government (e.g. from the district to federal level). This extensive body of original research is complemented by a wide array of other relevant source material that has likewise been consulted to inform this work and further evaluate the impacts of bottom-up energy democracy initiatives on the broader transition in Germany’s regions - and, by extension, nationally - as they continue to affect the pace and trajectory of the now-famous Energiewende. Ultimately, this study contributes an in-depth, ground-level analysis to the literature of key elements that have driven energy system change in Germany, shining a fresh light on the complex and interrelated nexus of sustained grassroots action, policy responses, and shifting sociopolitical realities that form the context in which the Energiewende has been (re)launched, reformed, and reimagined over the decades.