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Land abandonment, e.g. agricultural land abandonment, can result in various social and ecological impacts. It would thus be helpful if the extent and spatial pattern of future land abandonment could be projected. However, the trajectory of future land abandonment generally depends on various factors, including biophysical conditions and future changes in socioeconomic indicators in the area. In this study, we developed a general framework for a scenario-based land abandonment projection, featuring a coupled regional economic and spatially explicit land change modeling approach. We applied this framework in selected municipalities in Fukushima Prefecture, Japan, under two socioeconomic development scenarios (2014–2050): low population and economic growth (LL scenario) and high population and economic growth (HH scenario). The case study results, which are also visualized through a set of hot spot maps, revealed that agricultural land abandonment would be more intense under the HH scenario due to the much higher future decline in farmer population driven by the shift in people's employment and main source of livelihood. Under the LL scenario, residential and urban land abandonment would be more profound because of the much higher future decline in total population. In general, our results provide insights into some plausible future socioeconomic changes, their interplay and their consequent land abandonment in the case study area, which would be useful in the context of forward-looking adaptive development planning. The proposed framework can be applied to other case study areas. Unlabelled Image • A general framework for a scenario-based land abandonment projection is developed. • It was operationalized and applied in selected municipalities in Japan. • Under a high growth scenario, agricultural abandonment would be more intense. • Under a low growth scenario, urban land abandonment would be more profound. • The lessons learned on the application of the proposed framework are discussed. [ABSTRACT FROM AUTHOR]

We conducted this study to clarify the impact of regional climatic conditions on the energy consumption in the commercial sector, in Japan. For this research, we utilized the Database for the Energy Consumption of Commercial Buildings (DECC), which provided data on the individual buildings. The existing energy consumption pattern derived from the individual building data seems to have large variations depending on climatic conditions. Therefore, in this research, connections were made between the seasonal fluctuations in energy consumption as well as the seasonal temperature changes, in order to extract the components of variation due to temperature, and make a regional comparison. In addition, we prepared estimating equations for the energy consumption based on temperature. By applying the regional temperature data to these equations, the impact of temperature alone was extracted for comparison. The results showed that, for the types of buildings addressed in this research, even though there is a regionality to the ratio of increments during summer and winter with regard to heating and cooling, it is offset by summing them up. In this research, we were able to roughly determine the level of fluctuations in the energy consumption as an impact of temperature. [ABSTRACT FROM AUTHOR]

Although several central and local governments around the world have set long term low-carbon targets, they do not always have concrete plans toward the goals. In order to reduce greenhouse gas (GHG) emission significantly, various kinds of measures in various sectors will be required. Some measures reduce GHG emissions directly and are called “direct measures” in this study. To implement those direct measures by the target year, indirect measures are also required, such as economic incentives, regulations, and urban planning. This study proposes a method to develop a concrete long-term schedule of direct and indirect measures, a “roadmap,” based on the idea of backcasting. To formulate the implementation of and relations between measures, and to estimate the concrete schedule to achieve the targets, we developed a model called “backcasting tool” (BCT). Given the information of measures including GHG emission reduction by measures, required resource input and maximum total resource input, and so on, BCT estimates the progress of the measures from the current to the target year. The method was applied in Kyoto City with a low-carbon target, 40% reduction in CO2 emission from fossil fuel by 2030 compared with 1990. Using BCT, we showed that a detailed schedule consists of about 100 measures classified into six fields. The result shows it is possible to achieve the target if all the measures, both direct and indirect, are implemented in time. [ABSTRACT FROM AUTHOR]

Quality of life (QOL), although a complex and amorphous concept, is a term that warrants attention, especially in discussions on issues that touch on the impacts of climate change and variability. Based on the principles of RepOrting standards for Systematic Evidence Synthesis, we present a systematic review aimed at gaining insights into the conceptualization and methodological construct of previous studies regarding QOL and QOL-related indexes. We find that (i) QOL assessments vary in terms of conceptual foundations, dimensions, indicators, and units of analysis, (ii) social indicators are consistently used across assessments, (iii) most assessments consider indicators that pertain to the livability of the environment, and (iv) QOL can be based on objective indicators and/or subjective well-being, and on a composite index or unaggregated dimensions and indicators. However, we also find that QOL assessments remain poorly connected with climate-related issues, an important research gap. Our proposed "QOL-Climate" assessment framework, designed to capture the social-ecological impacts of climate change and variability, can potentially help fill this gap. [ABSTRACT FROM AUTHOR]

This study aims at the development of Thailand’s low-carbon society in 2030 by using the Asia-Pacific Integrated Model/Extended Snap Shot model for analysis of greenhouse gas (GHG) mitigation through renewable energy (RE) utilization. This article presents the potentials of RE in power generation, industrial, and transport sectors in Thailand for GHG mitigation in 2030. The deployment of the RE sources is used in the analyses with potentials of mini-hydro of 390 MW, wind power of 960 MW, solar power of 600 MW, biomass energy of 4,400 MW, biogas power of 144 MW, waste to power of 192 MW, bioenergy of 4,634 ktoe, and RE for thermal of 8,088 ktoe in 2030. According to the proposed development, the amount of GHG emissions is estimated based on business-as-usual (BAU) without mitigation measures and countermeasures with GHG mitigation options of adopted RE technologies available during 2005–2030. Results show that annual GHG emissions in the base year of 2005 are 185,983 kt-CO2. In 2030 the GHG emissions in the BAU scenario will increase to 563,730 kt-CO2or 3.03 times higher than the base year 2005. The GHG emissions in the 2030 can be decreased dramatically to 443,043 kt-CO2, and accounted for 21.4% of GHG reduction by deployment of RE technologies. [ABSTRACT FROM AUTHOR]

• Multiple energy demand monitoring systems were installed in residences in Bogor. • Electricity consumption time-series and equipment ratio in the city were estimated. • Reinforcement learning system for electricity demand estimation systems was created. • Hourly energy demand prediction system with scope for automatically improvement was developed. Tracking decarbonization effects requires a model for the identification of spatial energy demands on city facilities. However, most developing countries lack detailed discrete time and device-specific energy demand data. In this study, we installed multiple energy demand monitoring systems that could observe electricity demands at the device level in some residences in Bogor, Indonesia. The study aimed to estimate the time-series and equipment ratio of electricity consumption by households in the entire city based on the monitoring data. However, the number of households monitored was small, and therefore unlikely to be regarded as having a representative system. Therefore, we used questionnaire data to create monitored mimic data and increased the number of samples to estimate the energy demand characteristics of the entire city via spatial interpolation. In addition, we developed a reinforcement learning system for discrete time–electricity demand estimation systems for unmonitored households using a 5-step procedure; 1) Analyzing energy demand and its patterns from monitoring data, 2) Questionnaire-based surveying of households, 3) Estimation of energy demand and its patterns based on questionnaire responses in monitored households, 4) Development of a deep learning model that extends the results from (3) to unmonitored households using data fusion, and 5) Spatial interpolation of energy demand characteristics for all households in Bogor using a spatial statistics method. The spatial electricity demand of households was interpolated from GIS and high-resolution satellite data matching procedures. Based on this analysis, we developed an hourly energy demand prediction system that could be automatically improved by adding new data from the reinforced learning framework. [ABSTRACT FROM AUTHOR]

Abstract: Sustainability policies that have quantitative emission or carbon footprint measurements are important in view of climate change and current rapid urbanization in developing countries like Malaysia. Although sustainability policies are mentioned in national development plan as early as in the 1990s, many of these policies are piecemeal and expressed qualitatively. In achieving environmental sustainability, planning of city or a region can be more functional and implementable by using low carbon city concept. Planning a low carbon city uses sustainable development principles; this demonstrates a high level of energy efficiency using low carbon energy sources and production technologies. It also adopts patterns of consumption and behavior that are consistent with low levels of greenhouse gas emissions in the urban areas. The planning of the fast developing region of Iskandar Malaysia, which is located in southern part of Peninsular Malaysia, provides a good opportunity for urban managers to incorporate the ideas of low carbon cities in this proposed high growth urban conurbation. This paper aims to explore implementation of the concept and vision of low carbon scenarios and examine the strategies toward the reduction of CO2 emission. This study also prepares a quantitative scenario study on the establishment of low carbon urban development in Iskandar Malaysia. [Copyright &y& Elsevier]