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Integrated assessment of resilience to drought by coupling hydro-economic and macroeconomic models
Get PDFHydro-economic models combine biophysical and socioeconomic variables and are tools that inform decision-making related to water resources planning. This study analyses the coupling of a hydro-economic model of the Guadalquivir River Basin (GRB) in southern Spain with a Computable General Equilibrium (CGE) macroeconomic model, applied to a drought situation and different water management policy scenarios. The two models are interconnected through changes in land use and crop prices. Results show that when the macroeconomic price effects are included in the analysis, there is an improvement in producers’ gross margin across all scenarios, with some scenarios (Drought Management Plan, Increased Efficiency, and Optimal Allocation) even registering a higher gross margin for irrigated land than the baseline scenario without drought (+4.5 %; +3.2 % and +2.6 %, respectively). However, this increase is not uniform across all crops; rather, the rise in gross margin for certain crops contributes to an overall average producers’ gain throughout the entire basin. Thus, by considering the price effect, the market equilibrium generated in the coupled model attenuates the microeconomic impact of a drought for producers. This improvement in producer surplus translates into a worsening of consumer surplus between 33 and 67 M EUR depending on the scenario. Finally, the Optimal Allocation scenario is the one in which welfare decreases the least (5 M EUR)
Exploring climate futures with deep learning
No full textGlancing forward to view alternative futures for limiting global warming requires understanding complex societal–environmental systems that drive future emissions. Now a study explores the potential, and limits, of deep learning to generate core characteristics of these futures
Using deep learning to generate key variables in global mitigation scenarios
No full textIntegrated assessment models (IAMs) are the dominant tools for projecting mitigation scenarios. However, IAM-based scenarios often face challenges such as modelling biases and large computational burden. Here we develop a deep learning framework to generate key variables through synthetic mitigation scenarios aligned with the Sixth Assessment Report (AR6) Scenarios Database. By analysing 1,202 scenarios from a diverse set of IAMs, we select key drivers that enable a more detailed sectoral representation. Next, we trained three generative deep learning models to produce 30,000 synthetic scenarios at low computational cost across various IPCC AR6 climate categories, replicating variable distributions and correlations while also demonstrating physical consistency in power sector variables through internal validation checks. We found that the variational autoencoder achieved the highest label transferring accuracy among three frameworks. This study illustrates the potential of deep learning to complement IAM approaches and provides a basis for handling complex mitigation scenario generation tasks
Optimal Forecast Combination for Japanese Tourism Demand
Get PDFThis study introduces a novel forecast combination method for monthly Japanese tourism demand, analyzed at both aggregated and disaggregated levels, including tourist, business, and other travel purposes. The sample period spans from January 1996 to December 2018. Initially, the time series data were decomposed into high and low frequencies using the Ensemble Empirical Mode Decomposition (EEMD) technique. Following this, Autoregressive Integrated Moving Average (ARIMA), Neural Network (NN), and Support Vector Machine (SVM) forecasting models were applied to each decomposed component individually. The forecasts from these models were then combined to produce the final predictions. Our findings indicate that the two-stage forecast combination method significantly enhances forecasting accuracy in most cases. Consequently, the combined forecasts utilizing EEMD outperform those generated by individual models
Global land cover maps do not reveal mining pressures to biodiversity
Get PDFGlobal land cover maps are key inputs into the biodiversity metrics used by the private sector to align their performance with conservation goals and targets. These maps utilize classification systems depicting combinations of ‘natural’ (vegetation, water bodies) and ‘anthropogenic’ (agriculture and built-up land) cover types, but often miss intensive pressures on biodiversity, such as mining. Here, we reveal that more than half (56–77%) the global land area disturbed by mining is classified by land cover maps as ‘natural’, suggesting metrics based on these maps likely overestimate the current state of biodiversity and underestimate opportunities to improve it. The proportion of mining land classified as natural varies by continent (e.g. 46% in Europe; 69% in Australia), further biasing initial screening efforts to identify where to mitigate negative impacts of mining. Improving the spatial and temporal resolution of land cover maps and better integrating cumulative impact mapping into biodiversity metrics, rather than relying on land cover maps which are not designed to capture land use pressures, is necessary. Current biodiversity metrics that utilise global land cover maps must be supplemented and validated with local data on ecosystem extent and condition, as well as species abundance and extinction risk, through targeted field studies, particularly in regions with large mining sectors and significant biodiversity value
Optimization in Age-Structured Dynamic Economic Models
No full textAge-structured optimal control models experience increasing applications in various research fields including, e.g., demography, economics, operations research, epidemiology, and environmental economics. In this paper we present the mathematical theory and potential applications of age-structured optimal control models. We first state the general form of the problem and present the necessary optimality conditions. To illustrate the mathematical theory we introduce a toy model on air pollution, where consumption induces pollution which in turn negatively affects utility, fertility, and mortality. We solve the model analytically and present numerical simulations. The potential of an age-structure approach to solve non-standard optimal control models is demonstrated by considering optimal control models with random switches or time-lags and delays
Habitability for a connected, unequal and changing world
Get PDFAs global climate change intensifies, the question of what makes a place habitable or uninhabitable is critical, particularly in the context of a potential future climate outside the realm of lived experience, and the possible concurrent redistribution of populations partly associated with such climatic shifts. The concept of habitability holds the potential for advancing the understanding of the societal consequences of climate change, as well as for integrating systemic understandings and rights-based approaches. However, most ways of analyzing habitability have shortcomings in terms of in-depth integration of socio-cultural aspects and human agency in shaping habitability, in failing to address spatial inequalities and power dynamics, and in an underemphasis of the connectedness of places. Here we elaborate habitability as an emergent property of the relations between people and a given place that results from people’s interactions with the material and immaterial properties of a place. From this, we identify four axes that are necessary to go beyond environmental changes, and to encompass socio-cultural, economic, and political dynamics: First the processes that influence habitability require a systemic approach, viewing habitability as an outcome of ecological, economic, and political processes. Second, the role of socio-cultural dimensions of habitability requires special consideration, given their own operational logics and functioning of social systems. Third, habitability is not the same for everyone, thus a comprehensive understanding of habitability requires an intersectionally differentiated view on social inequalities. Forth, the influence of external factors necessitates a spatially relational perspective on places in the context of their connections to distant places across scales. We identify key principles that should guide an equitable and responsible research agenda on habitability. Analysis should be based on disciplinary and methodological pluralism and the inclusion of local perspectives. Habitability action should integrate local perspectives with measures that go beyond purely subjective assessments. And habitability should consider the role of powerful actors, while staying engaged with ethical questions of who defines and enacts the future of any given place
The inoculation dilemma: Partial vs Full immunization during the early rollout in a pandemic
No full textCOVID-19 demonstrated the extent to which a pandemic can affect billions of lives worldwide. Vaccinations are an effective intervention that reduces the burden of the disease on the population. However, the low availability of vaccine doses coupled with an emerging infection wave calls for efficient dose allocation. We study the tradeoffs between prioritization of partial or full immunization while allocating limited doses with the help of an augmented SIR model. We define the term allocation ratio as the ratio of doses allocated for partial immunization as a proportion of the total available doses. Optimal control theory is used to derive the path traversed by the allocation ratio throughout the vaccine administration program. Numerical insights are obtained by introducing the case study of the Indian state of Tamil Nadu. Results indicate a preference towards full immunization when the active infections are low, while a switch to exclusive partial immunization is observed as the infection wave grows. Sensitivity analysis shows that factors like reduced vaccine availability, higher transmission rate, and high first-dose efficacy promote a quicker switch. The results also indicate significant potential savings of around ₹710 billion (∼ $8.46 billion) in mortality losses compared to the more widely followed pro-rata allocation policies. Hence, our study contributes to the growing discussion around the optimal strategy for vaccine administration with a focus on dose prioritization. The results of our research can help policymakers determine the allocation of limited available doses when faced with rising infection numbers during future pandemics
Statistical Distribution of Urban Area Reveals a Converging Trend of Global Urban Land Expansion
Get PDFUrban land expansion is a major driver of many environmental and societal changes that challenge human well-being and sustainable development, but its evolutionary process and dynamics are neither clear nor well-integrated into urban science quantitatively. We analyzed the global urban extent data based on nighttime lights to examine the statistical distribution of urban land area at the global scale, and in 13 regions and countries over 29 years. The results reveal a converging temporal trend in urban land expansion from subnational to global scales, characterized by a coherent shift of urban area distribution from an initial power law toward an exponential distribution. This trend is well captured by a unified mathematical model based on the shifted power law distribution function and is reflected in the gradual predominance of medium-size cities over small-size cities in the configuration of urban systems across the world. The shift of urban area distributions bears the consequence of reduced urban system stability and resilience, and can be linked to increasing exposure of urban populations to extreme heat events and air pollution. These changes are likely to be driven by the increasing influence of external economies of scale associated with globalization. The findings challenge the status quo of land urbanization practices and emphasize the importance of medium-size cities in urban planning
Assessing uncertain technological progress in the decarbonization pathway of China's hydrogen energy system
No full textHydrogen energy is regarded as a promising solution for decarbonizing hard-to-abate sectors, while its role in the energy transition remains debatable. One of the key reasons is that uncertainty in technological progress has significant impacts on investment decision-making. To assess these effects, this study employs the MESSAGEix framework to develop a hydrogen energy system optimization model in China's context and integrates it with a stochastic scenario-tree generation method to assess the effects of uncertain technological progress on decarbonizing China's hydrogen energy system. The modeled system covers a full range of hydrogen production and consumption associated with different technical options for decarbonization, i.e., renewable energy-based water electrolysis (green hydrogen) and fossil-derived hydrogen coupled with carbon capture and storage. The model simulates a wide range of stochastic crucial cost metrics under the carbon-neutral constraint and compares it to a baseline without an emission constraint. Results show that disruptive technological breakthroughs in renewable electricity generation are essential to decarbonizing the hydrogen production system. The proposed hybrid modeling approach proves that computing is effective and could be applied to many other stochastic programming problems in long-term energy system planning