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Many developing countries have emphasis on distributed generation (DG) technology for their generation expansion planning. The planning considerations and judicious choice of attributes are dictated by prevailing conditions. The attributes considered are capital costs, energy not served per annum, and profits from injecting power into the grid at peak load, all of which are important for a developing country. The uncertain futures considered are three possible loading conditions, which can be low, medium and high. Different scenarios (plans) are generated by various combinations of configurations. DGs can be configured as stand-alone mode, hybrid operation, or micro-grid formation with or without grid connection. With the increased complexities in DG planning options along with the multiple attributes to be accounted, more sophisticated techniques other than conventional economic analysis are needed to arrive at correct decisions by decision makers. The analytical hierarchy process (AHP) is used for obtaining relative weights in an objective way. Further, the statistical method like interval-based multi-attribute decision making with tradeoff analysis is used for shortlisting the feasible plans and identifying the most appropriate plan. It is proposed to use the weights obtained from AHP for finding the performance efficiencies in data envelopment analysis (DEA) for evaluating the plans. A new composite utility function is proposed to resolve cases where performance efficiency is insufficient for evaluation in DEA application. The sample system is derived with reference to a rural electrification scheme in India. The assessment of plans is presented and discussed. The comparative strengths and weaknesses of the methods are reported on the basis of the results obtained.

This thesis addresses how a new method for the evaluation of policies with a very long term impact on the Dutch Inland Waterway Transport (IWT) system can be developed. It proposes an outline for a very long term transport model, prepares a number of very long term scenarios, and indicates that a different perspective on economic growth and future discounting is required to obtain realistic projections and develop sensible policies for issues with a very long term impact. Extended summary report: http://resolver.tudelft.nl/uuid:ef349716-b484-4820-a02a-d1cd13bc51b8 Civil Engineering and Geosciences Hydraulic Engineering

Sustainable urbanization policies and strategies are posited as a major tool by which to achieve the sustainable development of growing towns and cities. A major challenge for sustainable urbanization policies and strategies is how to address the complexity of urbanization, especially the ongoing growth of informal settlements and slums in developing countries. It is acknowledged that those living their lives in such housing and settlements suffer greater levels of spatial, economic and social exclusion from the benefits of urbanization that other segments of the urban population. Using a case study approach, this paper examines the range of challenges associated with the growth of informal settlements and slums, seeking to understand how they are positioned via upgrading policies in city urbanization plans and strategies in Indonesia’s third largest city, Bandung. The research finds that there has been a shift in kampung and slum upgrading policy from in-situ solutions to vertical housing towers which appear incompatible in accommodating the way of life practiced in kampung adaptive urbanism contexts. The manner in which city governments manage informal settlements and slums by seeking to reshape and restructure the lifestyles of residents to align with formal market measures has a major impact on existing disadvantaged communities. The paper concludes with a call for greater leadership, political commitment and recognition of contextual responses when developing slum upgrading policies set within urbanization policies and strategies branded as sustainable.

Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food insecurity is essential to be able to trigger early actions, for example, by humanitarian actors. To measure the actual state of food insecurity, expert and consensus-based approaches and surveys are currently used. Both require substantial manpower, time, and budget. This paper introduces an extreme gradient-boosting machine learning model to forecast monthly transitions in the state of food security in Ethiopia, at a spatial granularity of livelihood zones, and for lead times of one to 12 months, using open-source data. The transition in the state of food security, hereafter referred to as predictand, is represented by the Integrated Food Security Phase Classification Data. From 19 categories of datasets, 130 variables were derived and used as predictors of the transition in the state of food security. The predictors represent changes in climate and land, market, conflict, infrastructure, demographics and livelihood zone characteristics. The most relevant predictors are found to be food security history and surface soil moisture. Overall, the model performs best for forecasting Deteriorations and Improvements in the state of food security compared to the baselines. The proposed method performs (F1 macro score) at least twice as well as the best baseline (a dummy classifier) for a Deterioration. The model performs better when forecasting long-term (7 months; F1 macro average = 0.61) compared to short-term (3 months; F1 macro average = 0.51). Combining machine learning, Integrated Phase Classification (IPC) ratings from monitoring systems, and open data can add value to existing consensus-based forecasting approaches as this combination provides longer lead times and more regular updates. Our approach can also be transferred to other countries as most of the data on the predictors are openly available from global data repositories.

Climate change is threatening the health of current and future generations of children. The most recent evidence from the Lancet Countdown: Tracking Progress on Health and Climate Change finds declining trends in yield potential of major crops, rising heatwave exposures, and increasing climate suitability for the transmission of infectious diseases, putting at risk the health and wellbeing of children around the world. However, if children are considered at the core of planning and implementation, the policy responses to climate change could yield enormous benefits for the health and wellbeing of children throughout their lives. Child health professionals have a role to play in ensuring this, with the beneficiaries of their involvement ranging from the individual child to the global community. The newly established Children in All Policies 2030 initiative will work with the Lancet Countdown to provide the evidence on the climate change responses necessary to protect and promote the health of children.

The diversification of agri-food products through emerging chains has a fundamental social and economic role in Ecuador. A substantial amount of research focused only on examining critical factors in terms of agronomic and environmental performance. However, there is a shift in the agri-food chain perspective and study towards more sustainable models of production, logistics trade, and consumption. Aspects such as the socio-economic sustainability, level of collaboration between actors, an adequate chain configuration, and the employment of smart governance mechanisms show the weaknesses where stakeholders can propose enhancements. In this respect, socioeconomic and productive factors are consequential and still affecting the progress of these chains. Also, the current growth of market opportunities at the local and international level is a driver to support them by setting sustainable strategies. This study aimed to analyze socio-economic and production aspects to understand the dynamic across the emerging Inca berry (Physalis peruviana) chain located in Ecuador and bring forward potential strategies. Thus, chain vertical and horizontal dimensioning was introduced to contribute with relevant insights. The framework applied accounts with a revision of primary and support activities, and flows of high and low relevance. The investigation clustered pre-production, production, and post-production tiers. Also, it executed the food chain mapping, the identification of chain actors, and application of surveys at the supply chain levels to identify strengths and weaknesses based on specific socio-economic and productive variables. Results stated several viable long-term strategies. Examples of those strategies are the diversification of marketing channels, the intervention of academic institutions to improve efficiency, productivity, and the associations' empowerment. All of them aimed at circular economic models. The main research contribution is the application of the chain configuration to assess the chain performance comprehensively. Based on the results, our recommendation is incorporating new indicators to analyze the environmental and institutional components profoundly.

AbstractThe effects of rising global temperatures are becoming increasingly evident, with observable consequences such as the melting of polar ice caps, the occurrence of cyclones and hurricanes, desertification, and the destruction of ecosystems. The Italian economy is particularly vulnerable to the climate challenge, due to the prolonged slowdown in economic growth and the high unemployment that have plagued this economy over the last decades. Environmental innovation could be the key to tackling climate change, while at the same time promoting growth and employment. A comprehensive assessment of the effects of environmental innovation on growth and employment at the macroeconomic level should consider the compensation mechanisms associated with productivity gains, the substitution effects between more or less polluting goods, and the role of demand and consumer preferences. However, a comprehensive analysis that includes all of these direct and indirect effects of environmental innovation at the macroeconomic level is still lacking. This study aims to bridge this gap, introducing a structuralist computable general equilibrium model to simulate the effects of an increase in productivity and a change in consumer preferences in favour of less polluting industries in the Italian economy over the period 1995–2050. The results of the simulations indicate that a change in consumer preferences in favour of environmentally friendly goods in the Italian context may be more effective than an increase in productivity in stimulating demand, growth, and employment.