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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.

In industrialized countries, large amounts of mineral wastes are produced. They are re-used in various ways, particularly in road and earth constructions, substituting primary resources such as gravel. However, they may also contain pollutants, such as heavy metals, which may be leached to the groundwater. The toxic impacts of these emissions are so far often neglected within Life Cycle Assessments (LCA) of products or waste treatment services and thus, potentially large environmental impacts are currently missed. This study aims at closing this gap by assessing the ecotoxic impacts of heavy metal leaching from industrial mineral wastes in road and earth constructions. The flows of metals such as Sb, As, Pb, Cd, Cr, Cu, Mo, Ni, V and Zn originating from three typical constructions to the environment are quantified, their fate in the environment is assessed and potential ecotoxic effects evaluated. For our reference country, Germany, the industrial wastes that are applied as Granular Secondary Construction Material (GSCM) carry more than 45,000 t of diverse heavy metals per year. Depending on the material quality and construction type applied, up to 150 t of heavy metals may leach to the environment within the first 100 years after construction. Heavy metal retardation in subsoil can potentially reduce the fate to groundwater by up to 100%. One major challenge of integrating leaching from constructions into macro-scale LCA frameworks is the high variability in micro-scale technical and geographical factors, such as material qualities, construction types and soil types. In our work, we consider a broad range of parameter values in the modeling of leaching and fate. This allows distinguishing between the impacts of various road constructions, as well as sites with different soil properties. The findings of this study promote the quantitative consideration of environmental impacts of long-term leaching in Life Cycle Assessment, complementing site-specific risk assessment, for the design of waste management strategies, particularly in the construction sector.

II ABSTRACT Climate change is expected due to the increasing atmospheric concentrations of carbondioxide and other trace gasses, which lead to changes in the radioactive balance of the atmosphere. Such changes propagate further to those in temperature i and other climatic variables. Hydrologic systems and water resources are likely to be seriously impacted by global climate change. Such processes as surface runoff, precipitation, soil moisture, groundwater, water quality, and sea levels will be significantly exposed to effects of climate change. Eventually, these effects will have to be considered in water resources planning and management. The study presented stems from the above needs and addresses the problem of climate change-water resources interactions. It is intended here to investigate the possible effects of climate change on watershed scale hydrologic processes and water demand. Within this context, the current methods used in developed countries are applied to the case of the Gediz River Basin, and possible impacts of changes in climatic variables, i.e., precipitation and temperature, are investigated as they relate to runoff The results of the study should not considered as exact values to represent the effects of climate change. They are essentially `estimates` on `likely` effects of an expected climate change. However, the results also show that, if the prescribed climate change scenarios do occur in the future, they will have significant effects on the hydrology of the basin which, in turn, affects water demand for various water resources development plans. Accordingly, the study presented should be considered as an initial step towards assessment of climate change impacts and should be reaccomplished in future attempts towards any planning or management activity in the basin. ÖZET İklim değişikliği, atmosferdeki karbondioksit (CO2) ve diğer antropojen sera gazı konsantrasyonlarının giderek artması sonucu beklenmektedir. İklim değişikliğinin sonucunda beklenen en önemli olgu, sıcaklık, yağış, evapotranspirasyon, akış gibi temel iklimsel ve hidrolojik değişkenlerdeki muhtemel değişimlerdir. Bu etkilerin su kaynakları planlama ve yönetiminde değerlendirilmesi gerekmektedir. Sunulan çalışma, iklim değişikliğinin su kaynakları ile olan ilişkisini incelemektedir. Bu değişimlerden hidrolojik çevrimin ve su kaynaklarının gerek miktarı ve gerekse kalitesinin de etkilenmesi beklenmektedir. Dolayısıyla, küresel iklim değişikliğinin havza su dengesine ve su kaynaklarının planlama ve yönetimine de yansıması söz konusudur. Bu kapsamda sunulan çalışmada alt havza bazında sıcaklık ve yağış gibi iklim parametrelerinin, akıma olan etkileri incelenmiş ve duyarlılık analizi ile sonuçlar değerlendirilmiştir. 75

Devido à crescente preocupação com as alterações climáticas e à consequente mudança de paradigma, gradualmente são utilizados mais veículos elétricos e híbridos, como é o caso dos FCHEV (Fuel cell Hybrid Electric Vehicle). Como é característico nos veículos híbridos, a existência de duas fontes de energia faz com que seja crucial otimizar a distribuição de potência, sendo esta a chave para melhorar o desempenho do veículo. Assim, definiu-se como medida principal de desempenho o consumo equivalente em Gasoline Gallon Equivalent, que efetua um tradeoff entre o consumo de hidrogénio e o consumo ponderado de energia da bateria, em função do seu estado de carga. Tendo como principais objetivos o aumento do tempo de vida dos componentes e a redução do consumo do veículo, construiu-se uma estratégia de gestão energética em tempo real, baseada em programação dinâmica, com extração de regras de controlo pela response surface methodology e implementação de machine learning para a identificação dos tipos de ciclo de condução. A estratégia foi construída e simulada em Matlab, partindo da modelação do sistema e da implementação da função DPM (Dynamic Programming Matrix), desenvolvida pelo instituto ETH Zurich para efetuar a programação dinâmica. Seguidamente, foi utilizada a função stepwiselm e a app Regression Learner para extrair as regras de controlo e, finalmente, recorreu-se à app Classification Learner para identificar os ciclos de condução. Toda a estratégia foi complementada com o Matlab Coder, para fazer a transição do algoritmo para linguagem C, suportada pela ECU. Os resultados foram analisados no final de cada fase de implementação, validando a metodologia proposta. Assim, na fase de otimização demonstrou-se que é possível melhorar o consumo equivalente, relativamente ao algoritmo implementado no veículo, obtendo-se uma redução média superior a 15%, sem se demonstrarem alterações significativas no consumo de H2. A partir destes resultados, efetuou-se a extração de regras de controlo, utilizando duas estratégias distintas: regressões não lineares e árvores de decisão. No caso da primeira, não foi possível demonstrar que efetivamente o consumo equivalente é menor, apesar da percentagem de redução desse consumo ser em média superior a zero. No caso da segunda, a robustez do modelo de machine learning demonstrou que em média o consumo equivalente é menor do que no algoritmo atualmente presente no autocarro, sendo que a percentagem de redução em média ultrapassa os 10%. Com ambas as estratégias, as alterações no consumo de H2 não se mostraram significativas. Na fase de reconhecimento do ciclo de condução, utilizou-se uma árvore de decisão que foi analisada para diferentes tempos de decisão, demonstrando-se que com 600 e 300 segundos a identificação apresentou os melhores resultados de accuracy, sendo percetível que para 300 segundos será reduzido o espaço em memória na ECU para armazenamento dos parâmetros de condução, mostrando-se também ser mais preciso em cenários mais semelhantes com a realidade. Finalmente os testes de estrada demonstraram melhorias de 15.7% no consumo equivalente, 24.4% no consumo de H2 e 6.8% no rendimento, com a estratégia que utiliza regressões não lineares. No entanto, o algoritmo mais adequado seria o construído com árvores de decisão, que devido à sua complexidade não foi possível de implementar na ECU.

AbstractPhenolic compounds cause significant problems both in drinking water and wastewater due to their toxicity, high oxygen requirements, and low biodegradability. They are listed as primary pollutants by the United States Environmental Protection Agency and the European Union. In this study, the adsorption efficiency of 2,4‐dichlorophenol (2,4‐DCP) on activated carbon, which is commonly used in treatment plants, was investigated under different experimental conditions including adsorbent dose, initial phenol concentration, initial pH, and contact time. As a result of experimental studies, it was determined that the adsorption isotherm and kinetics could be perfectly fitted to Langmuir and the assumption of pseudo‐second order model, respectively. Then, the adaptive neuro‐fuzzy inference system (ANFIS) model was developed, which was the primary purpose of this study. The correlation between training and testing data and the ANFIS output was over 0.999. The generalization ability of the model was found to be 0.999. The input variables such as adsorbent dosage (14.2%), initial concentration (14.6%), initial pH (13.9%), and the contact time (57.2%) showed a higher effect on 2,4‐DCP removal efficiency in the sensitivity analysis. To summarize, modeling studies that are frequently preferred in treatment plants for the removal of different pollutants will reduce the number of experiments harmful to human health and save time, labor, and economy.

The aim of this study was to analyze whether, and if so, in what way risks would influence the design,costs and routing of CO2pipelines. This article assesses locational and societal risks of CO2pipelinetransport and analyses whether rerouting or implementing additional risk mitigation measures is themost cost-effective option. The models EFFECTS and RISKCURVES are used to estimate the dispersion andrisk, respectively. The pipeline routes are optimized by using the least cost path function in ArcGIS.This article evaluates three case studies in the Netherlands. The results show that pipelines transportingdense phase CO2(8–17 MPa) with a minimal amount of risk mitigation measures already meet the 10−6locational risk required in the Netherlands. 10−6locational risks of 135 m are calculated for intermediatepumping stations, handling 450 kg CO2/s (about 14 Mt CO2/year). In all the cases, pumping stations couldbe located along the pipeline route without any problem.For the cases studied transporting gaseous CO2(1.5–3 MPa) leads to larger 10−6locational risk distancesthan transporting dense phase CO2. This is caused by the large momentum behind a dense phase CO2release, leading to smaller but higher jet and to a higher mixing rate with the surrounding air than for agaseous CO2release.Based on our analysis, it can be concluded that dense phase CO2transport is safe if it is well organized.The risks are manageable and widely accepted under current legislation. In addition, risk mitigationmeasures, like marker tape and increased surveillance, are available which reduce the risk significantlyand increase the costs only slightly. Pipeline routing for gaseous CO2transport appears more challengingin densely populated areas, because larger safety zones are attached to it.

According to the second law of thermodynamics, all human activities cause exergy destructions, adding to additional root causes for carbon dioxide emissions responsibility. It means that current carbon dioxide concentrations are accurately observed, but the root causes and their potential solutions against global warming fall short of achieving the goals of the Paris agreement by almost 45% in terms of decarbonization efforts, as shown in this paper. This result applies to all activities, including the green facility concept. In this respect, the primary aim of this paper is to raise awareness about the essence of the Second Law of Thermodynamics in expanding the green facility concept to reach more effective and sustainable rating methodologies concerning the climate crisis. A new evaluating and rating model with a set of exergy-based green building metrics that relate additional carbon dioxide emissions to irreversible exergy destructions has been developed. Examples about apparently green buildings according to the First Law of Thermodynamics are given by showing that these buildings are not green due to additional carbon dioxide emissions responsibility due to exergy destructions. An airport terminal building case is elaborated. It has been shown that although part of the electricity comes from a third-party wind energy provider, it ends up with carbon dioxide emissions responsibility because it is not entirely used in exergy-rational demand points and compares less favorably with an on-site cogeneration system using natural gas by about 30% more emissions responsibility. The results and derivations of new metrics are discussed, which shed light on adding new criteria to existing green building certification programs.

Abstract As main contributors to greenhouse gas emissions, power and transportation are crucial sectors for energy system decarbonization. Their interaction is expected to increase significantly: plug-in electric vehicles add a new electric load, increasing grid demand and potentially requiring substantial grid upgrade; hydrogen production for fuel cell electric vehicles or for clean fuels synthesis could exploit the projected massive power overgeneration by intermittent and seasonally-dependent renewable sources via Power-to-Hydrogen. This work investigates the infrastructural needs involved with a broad diffusion of clean mobility, adopting a sector integration perspective at the national scale. The analysis combines a multi-node energy system balance simulation and a techno-economic assessment of the infrastructure to deliver energy vectors for mobility. The article explores the long-term case of Italy, considering a massive increase of renewable power generation capacity and investigating different mobility scenarios, where low-emission vehicles account for 50% of the stock. First, the model solves the energy balances, integrating the consumption related to mobility energy vectors and taking into account power grid constraints. Then, an optimal infrastructure is identified, composed of both a hydrogen delivery network and a widespread installation of charging points. Results show that the infrastructural requirements bring about investment costs in the range of 43–63 G€. Lower specific costs are associated with the exclusive presence of FCEVs, whereas the full reliance on BEVs leads to the most significant costs. Scenarios that combine FCEVs and BEVs lie in between, suggesting that the overall power + mobility system benefits from the presence of both drivetrain options.

The aggravating global problems of energy crisis, rising atmospheric greenhouse gas concentrations and accumulation of persistent waste have attracted the attention of scientists, policy-makers and global organisations to come up with effective and expeditious solutions to address these challenges. In this context, the development of sustainable technologies driven by renewable energy sources for the production of clean fuels and commodity chemicals from diverse waste feedstocks is an appealing approach towards creating a circular economy. Over the years, semiconductor photocatalysts based on TiO₂, CdS, carbon-nitrides (CNx) and carbon dots (CDs) have been widely used for the photocatalytic reforming (PC reforming) of pre-treated waste substrates to organic products, accompanied with clean hydrogen (H₂) generation. However, these conventional solar-driven processes suffer from major drawbacks such as low production rates, poor product selectivity, CO₂ release, challenging process and catalyst optimisation, and harsh waste pre-treatment conditions, which limit their commercial applicability. These challenges are tackled in this thesis with the introduction of new and efficient photoelectrochemical (PEC) and chemoenzymatic processes for reforming a diverse range of waste feedstocks to sustainable fuels. Solar-driven PEC reforming based on halide perovskite light-absorber is first developed as an attractive alternative to PC reforming. The PEC systems consist of a perovskite|Pt photocathode for clean H₂ production and a Cu-Pd alloy anode for reforming diverse waste streams, including pre-treated cellulosic biomass, polyethylene terephthalate (PET) plastics, and industrial by-product glycerol into industrially-relevant, value-added chemicals (gluconic acid, glycolic acid and glyceric acid) without any externally applied bias or voltage. Additionally, the single light-absorber PEC systems can also convert the airborne waste stream and greenhouse gas CO₂ to diverse products with the simultaneous reforming of PET plastics with no applied voltage. The perovskite-based photocathode enables the integration of different CO₂ reduction catalysts such as a molecular cobalt porphyrin, a Cu-In alloy and formate dehydrogenase enzyme, which produce CO, syngas and formate, respectively. The versatile PEC systems, which can be assembled in either a ‘two-compartment’ or standalone ‘artificial leaf’ configurations achieve 60‒90% oxidation product selectivity (with no over-oxidation) and >100 µmol cm‾² h‾¹ product formation rates, corresponding to 10²‒10⁴ times higher activity than conventional PC reforming systems. In addition to developing PEC platforms, this thesis also explores avenues for circumventing the harsh alkaline pre-treatment strategies (pH >13, 60‒80 ºC) adopted for photoreforming waste substrates. For this purpose, a chemoenzymatic pathway is introduced whereby PET and polycaprolactone plastics were deconstructed using functional enzymes under benign conditions (pH 6‒8, 37‒65 ºC), followed by PC reforming using Pt loaded TiO₂ (TiO₂|Pt) or Ni₂P loaded carbon-nitride (CNx|Ni₂P) photocatalysts. The chemoenzymatic reforming process demonstrates versatility in upcycling polyester films and nanoplastics for H₂ production at high yields reaching ∼10³‒10⁴ µmol gsub‾¹ and activities at >500 µmol gcat‾¹ h‾¹. The utilisation of enzyme pre-treated plastics also allowed the coupling of plastic reforming with photocatalytic CO₂-to-syngas conversion using a phosphonated cobalt bis(terpyridine) co-catalyst immobilised on TiO₂ (TiO₂|CotpyP). Finally, moving beyond solar-driven systems, a bio-electrocatalytic flow process is demonstrated for the conversion of microbe pre-treated food waste to ethylene (an important feedstock in the chemical industry) on graphitic carbon electrodes via succinic acid as the central intermediate. In conclusion, with its focus on improving efficiencies, achieving selective product formation, building versatile platforms, diversifying substrate and product scope, and reducing carbon footprint and economic strain, this thesis aims to bring sustainable waste-to-fuel technologies a step closer to commercial implementation.