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While climatic conditions are believed to have some influence on triggering conflicts, the existing empirical results on the nature and statistical significance of their explanatory role are not conclusive. We construct a dataset for a sample of 139 countries which records the occurrence of an armed conflict, the annual average temperature and precipitation levels, as well as the relevant socioeconomic, demographic, and geographic measures over the 1961–2011 period. Using this dataset and controlling for the effect of relevant nonclimate variables, our comprehensive econometric analyses support the influencing role of climatic factors. Our results are robust and consistent with the hypothesis that climate warming is instrumental in raising the probability of onset of internal armed conflicts and suggests that, along with regulating population size and promoting political stability, controlling climate change is an effective factor for inducing peace by way of curtailing the onset of armed conflicts.

This research will analyze the traditional Iranian buildings according to the climatic factors by the use of graph theory. By this way, the hypothesis that climate factor has a major effect on the organization of the spaces in traditional Iranian buildings will be tested. Access graphs have been used to clarify the connectivity and depth of a building’s spaces from the socio-cultural point of view. However, it cannot be applied to climate studies. Thus, this study developed the existing technique to define building layouts in terms of climate and thermal comfort. The thermal comfort was graphically evaluated by the two main factors like solar gain and wind effect, with the use of a simple multi-attribute rating technique. All the analysis had been done in the interval of zero (the worst condition) to three (the best condition). The proposed orientation-weighted graph method proved that the thermal comfort factors of the buildings under study match the seasonal movements of their inhabitants. Consequently, the developed orientation-weighted graph method can be used to study space organization in traditional Iranian building in terms of solar gain and wind effect.

The climate change and related impacts are experienced around the world. There arise different triggering factors to climate change and impact. The purpose of this study is to figure out how changes in vegetation cover may or may not have an impact to climate change. The research will produce ideas for vegetation preservation and replant.

The investigation was probed for 34 years’ time period starting from the year 1981 to 2015. After testing and checking for serial autocorrelation in the vegetation data series, Mann–Kendal nonparametric statistical evaluation was carried out to investigate vegetation cover trends. Sen’s method was deployed to investigate the magnitude of vegetation cover change in natural differential vegetation index (NDVI) unit per year. Furthermore, the ArcGIS spatial analysis tools were used for the calculation of mean NDVI distribution and also for carrying out the spatial investigation of trends at each specific location within the study region.

The yearly mean NDVI during the study period was observed to have a decreasing trend. The mean NDVI value ranges between 0.32 and 0.98 NDVI unit, and hence, this means from less or poor vegetated zones to higher or healthier vegetated zones. The mean NDVI value was seen decreasing toward the highlands regions. The NDVI-rainfall correlation was observed to be stronger than the NDVI-temperature correlation. The % area coverage of NDVI-rainfall positive correlation was higher than the negative correlation. The % area coverage of NDVI-temperature negative correlation was higher than the positive correlation within the study region. Rainfall is seen as a highly influencing climatic factor for vegetation growth than the temperature within the study region.

This study in this country is a new approach for climate change monitoring and planning for the survival of the people of Papua New Guinea, especially for the farmer and those who is living in the coastal area.

The argument presented in this paper is based on distinctive and exploitable differences which merit putting eco-tourists, eco-resorts and the coastal tropics into categories of their own. Such differences should inform planning and design process when working in this environment, which is both very sensitive and valuable. The paper aims to describe the main characteristics of the coastal tropics as a climate targeted by eco-tourism. Differences between eco-tourists and residents are presented through definition of comfort. Climatic and other factors influencing comfort limits are at the core of discussion, in which passive design is seen as the most appropriate response to challenges of the tropical coast setting. The design opportunities for the desirable climate modifications in eco-friendly resorts together with some passive design features are briefly presented. These architectural design solutions are set against theoretical principles specific to tropical coastal regions. The focus is on human responses to environmental factors, and on their implications. The paper concludes with a few recommendations aimed to deliver indoor conditions consistent with climatic preferences of itinerant environmentally conscious users of buildings in the coastal tropics. Such an approach is expected to minimize impacts the facility will make on the environment.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

The purpose of this paper is to propose a novel resource availability assessment for supply chain (SC) configuration. This approach involves understanding both local resource availability and the demand-side implications of supplying global/regional markets as part of a more holistic SC design activity that incorporates local environmental factors.

The proposed framework was derived from literature analysis, bridging relevant literature domains – natural capital theory, industrial ecology and SC configuration – in order to develop design rules for future resource-constrained industrial systems. In order to test the proposed framework, an exploratory case study, based on secondary data, was conducted.

Research findings suggest that this approach might better identify relationships and vulnerabilities between natural resource availability and the viability of regional/global SCs. The research suggests that natural resource availability depends upon three elements – local resource consumption, global resource demand and external environmental factors.

The framework has two main limitations. The current work is focussed on a single industry case study used to exemplify the approach. Second, the framework does not consider other possible industries, which might enter or leave the specific location during the company’s operation. Furthermore, no assessment was made of the migration of populations within the area.

For practitioners, such as those in the agri-food sector, the resource availability assessment framework informs SC configuration design. For policymakers, the research aims to provide policy guidelines, which can help to improve water-saving strategies for a particular region. At a broader societal level, the research raises awareness of resource scarcity amongst industrial players and the wider public.

A resource availability assessment framework has been proposed, suggesting that the dynamics of both global and local resource demand, in conjunction with changing local environmental factors, can over time significantly deteriorate a firm’s natural resource impact on the local environment. Thus, the framework seeks to deliver mechanisms to evaluate potential vulnerabilities and solutions available to firms using a more proactive SC design method and to apply reconfiguration processes that account for natural resources, based primarily on network and resource attributes.

The purpose of this paper is to examine the market response of apple growers to price and price risk along with weather factors and weather risk in the state of Jammu & Kashmir. In other words, it tries to find the both short-run and long-run price elasticities of apples' market arrival and also the elasticity with respect to price-risk and weather-risk variables.

This paper uses the bound test approach of “auto-regressive distributed lag” (ARDL) model. Monthly data on market arrival of apples and respective prices along with other nonprice factors are used.

The bound test approach of ARDL confirms the existence of long-run relationship between the market arrival of apples and price and nonprice factors. The market response to price is found to be inelastic both in shortrun and longrun. The risk coefficients are negative indicating that apple growers are risk averse. However, they do respond strongly to weather risk than price risk.

Weather insurance must be provided to the apple growers to safeguard their production loss due to weather risks. Proper infrastructure in the form of storage facilities, marketing information, transport and communication to local markets should be provided to them. Unavailability of data at the district level poses a great difficulty to have a panel data analysis. But future research can be initiated to bridge this gap.

This paper considers the market response of apple growers under both price risk and weather risk which is first in its nature. The authors have not found any other paper discussing this in the case of apple in India.

This paper aims to investigate how climatic, market and policy factors interact to determine food production in Togo. Specifically, we estimate acreage and yield response to market prices, weather and policy changes for maize and rice.

We use panel data estimators in a Seemingly Unrelated Regressions Equation (SURE) model with region-level data from the Food and Agriculture Organization statistics department and the National Oceanic and Atmospheric Administration (NOAA) of the US Department of commerce.

We found lower fertilizer price and higher grain price effects on maize acreage and yield. In addition, we found a positive effect of expected rice price on both its acreage and yield. As expected, rainfall during planting months has a significant impact on both maize (April) and paddy (May) acreage allocations. Similarly, total rainfall during the growing season has a positive impact on both maize and paddy yields. Moreover, recent agricultural policy initiative designed to boost domestic food production has significantly increased acreage and yield for maize, and yield for paddy, especially the strategy for agricultural growth.

The dataset includes region-level observations from 1991 to 2012 which limits the observation span. However, we had enough variability in key variables to determine the estimated coefficients.

Although the dataset is limited in time (1991–2012) and uses national-level output prices, this investigation reveals that cropland allocation to maize and rice is sensitive to fertilizer and grain prices, weather expectations and policy interventions. These findings provide evidence for sustainable food production and productivity enhancement in Togo.

Understanding drivers of cropland allocation and cereal yield contribute to better food security and poverty reduction in developing countries, especially Togo.

Prior to this study, little was known on the effect of price, climate and policy on cropland allocation in Togo. This investigation contributes significantly to filling this knowledge gap and provides insights for effective interventions.

This paper aims to address Passive Design Strategies (PDSs) in the traditional houses of Sabzevar and to assess the adaptation level of these strategies to the climate of the region.

Identifying the Sabzevar climate, five samples of traditional houses have been chosen to be analyzed via two stages. In stage one, the efficiency of each strategy is weighted through qualitative analysis, and in stage two, the houses are simulated in EnergyPlus 9.3.0 to quantitatively evaluate their heating and cooling performances.

The obtained results from the energy performance analysis of the case studies indicate that the houses present diverse energy performances in different seasons. Those buildings with PDSs for both cold-arid and hot-arid climates, however, are more adaptable cases to the climate of the region.

The results of this study are expected to provide a basis of materials and methods for the climatic assessment of the traditional buildings, specifically traditional houses and will open new doors to future studies about the integration of these potential PDSs with the new technological developments and climate considerations as well as protecting the conservation policies of these buildings by means of optimizing and improving their energy performance and implementing effective retrofit scenarios.

Over the last decades, there has been an increasing interest among scientists on the linkage between population health and climate and environmental factors, as well as health impacts of climate change and climate variability. Numerous studies have been done and substantial results achieved, but mostly in the developed countries, and not much quantitative evidence or assessment of the impacts at national and local levels has been provided for developing countries.