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This study seeks to provide a robust piece of evidence of forest depletion in Ghana and its associated driver intensities to inform national policy decisions towards achieving Sustainable Development Goal (SDG) 15 and beyond.

Using a representative sample size of 733 households, which was obtained with the aid of a structured questionnaire, a descriptive analysis is used to show the evidence of forest depletion. For robustness purposes, the geographic information system (GIS) is used to provide a piece of remote sensing evidence to substantiate the claim. In addition, an ordered probit regression model is estimated given the ranked nature of the responses to determine the drivers of forest depletion.

The results provide evidence that the urban forests in the Greater Accra Region (GAR) of Ghana have been depleted. Overall, 44% argued that the depletion of the forests is high, 30% indicated that the depletion is moderate, while 26% indicated that the depletion is low. Consistent with the literature, the ordered probit regression results show that human behaviour, climate change and institutional failure are the driver intensities of forest depletion in the Region. Besides, the authors find an increasing order effect for all three drivers. Using a descriptive analysis, majority of the respondents posited that human behaviour is the main driver intensity, followed by climate change and then institutional failure. This study recommends the need for education and advocacy, community participation, law enforcement, resource mobilization, modern adaptation strategies and internalization of externalities as a way of controlling the drivers of forest depletion.

The study uses remote sensing techniques to provide empirical evidence of protected forest depletion in the GAR, Ghana. In addition, an ordered probit regression is used to identify the driver intensities that explain the depleted protected forests in the region.

Construction projects usually suffer delays, and the causes of these delays and its cost overruns have been widely discussed, the weather being one of the most recurrent. The purpose of this paper is to analyze the influence of climate on standard construction work activities through a case study.

By studying the extent at which some weather variables impede outdoor work from being effectively executed, new maps and tables for planning for delays are presented. In addition, a real case regarding the construction of several bridges in southern Chile is analyzed.

Few studies have thoroughly addressed the influences of major climatic agents on the most common outdoor construction activities. The method detailed here provides a first approximation for construction planners to assess to what extent construction productivity will be influenced by the climate.

Although this study was performed in Chile, the simplified method proposed is entirely transferable to any other country, however, other weather or combinations of weather variables could be needed in other environments or countries.

The implications will help reducing the negative social, economic and environmental outcomes that usually emerge from project delays.

Climatic data were processed using extremely simple calculations to create a series of quantitative maps and tables that would be useful for any construction planner to decide the best moment of the year to start a project and, if possible, where to build it.

This study aims to examine the impacts of climate change (CC), measured average annual rainfall, average annual temperature and carbon dioxide (CO₂e) on cereal production (CPD) in Bangladesh by using the annual dataset from 1988–2014, with the incorporation of cereal cropped area (CCA), financial development (FD), energy consumption (EC) and rural labor force as important determinants of CPD.

This study used an auto-regressive distributive lag (ARDL) model and several econometric approaches to validate the long- and short-term cointegration and the causality directions, respectively, of the scrutinized variables.

Results of the bounds testing approach confirmed the stable long-term connections among the underlying variables. The estimates of the ARDL model indicated that rainfall improves CPD in the short-and long-term. However, CO₂e has a significantly negative impact on CPD both in the short-and long-term. Results further showed that temperature has an adverse effect on CPD in the short-term. Among other determinants, CCA, FD and EC have significantly positive impacts on CPD in both cases. The outcomes of Granger causality indicated that a significant two-way causal association is running from all variables to CPD except temperature and rainfall. The connection between CPD and temperature is unidirectional, showing that CPD is influenced by temperature. All other variables also have a valid and significant causal link among each other. Additionally, the findings of variance decomposition suggest that results are robust, and all these factors have a significant influence on CPD in Bangladesh.

These findings have important policy implications for Bangladesh and other developing countries. For instance, introduce improved cereal crop varieties, increase CCA and familiarizes agricultural credits through formal institutions on relaxed conditions and on low-interest rates could reduce the CPD’s vulnerability to climate shocks.

To the best of the authors’ knowledge, this study is the first attempt to examine the short- and long-term impacts of CC on CPD in Bangladesh over 1988–2014. The authors used various econometrics techniques, including the ARDL approach, the Granger causality test based on the vector error correction model framework and the variance decomposition method.

Global climate change characterized by an increase in temperature has become the focus of attention all over the world. China is a sensitive and significant area of global climate change. This paper specifically aims to examine the association between agricultural productivity and the climate change by using China’s provincial agricultural input–output data from 2000 to 2019 and the climatic data of the ground meteorological stations.

The authors used the three-stage spatial Durbin model (SDM) model and entropy method for analysis of collected data; further, the authors also empirically tested the climate change marginal effect on agricultural productivity by using ordinary least square and SDM approaches.

The results revealed that climate change has a significant negative effect on agricultural productivity, which showed significance in robustness tests, including index replacement, quantile regression and tail reduction. The results of this study also indicated that by subdividing the climatic factors, annual precipitation had no significant impact on the growth of agricultural productivity; further, other climatic variables, including wind speed and temperature, had a substantial adverse effect on agricultural productivity. The heterogeneity test showed that climatic changes ominously hinder agricultural productivity growth only in the western region of China, and in the eastern and central regions, climate change had no effect.

The findings of this study highlight the importance of various social connections of farm households in designing policies to improve their responses to climate change and expand land productivity in different regions. The study also provides a hypothetical approach to prioritize developing regions that need proper attention to improve crop productivity.

The paper explores the impact of climate change on agricultural productivity by using the climatic data of China. Empirical evidence previously missing in the body of knowledge will support governments and researchers to establish a mechanism to improve climate change mitigation tools in China.

The purpose of this study is to evaluate rice farmers’ perceptions on the manifestations of the climate change and identify efficient strategies and determinants of adoption of these strategies in the Republic of Benin.

Surveys were conducted using participatory research appraisal tools and techniques, such as direct observation, individual interviews and field visits through a questionnaire for data collection. A total of 418 rice farmers across 39 villages located in the three climatic zones of the Republic of Benin were interviewed. Farmers’ perceptions, temperature from 1952 to 2018 and rainfall from 1960 to 2018 data obtained from meteorological stations were analysed using descriptive and inferences statistics.

All the surveyed farmers were aware of climate change and perceived diverse manifestations including the delay in rainfall regarded as the most important risk. They perceived that deforestation, no respect for the laws of nature and desacralization of morals, no respect for cultures and the traditional rainmakers are the main causes of climate change. The disruption of agricultural calendar and the reduction in rice yield were perceived as the main impacts of climate change in rice production. They used various approaches to adapt and mitigate climate change effects. The adoption of adaptation strategies was influenced either negatively or positively by the household size, land size, education level, membership to rice farmer’s association, training in rice production, access to extension services, use of improved varieties and the location in climatic zones.

For each climatic zone of the Republic of Benin, weather data were collected in only one meteorological station.

The study showed that it is important to educate rice farmers on the scientific causes of climate change for better resilience. There is an urgent need to train rice farmers in irrigation and water management techniques to cope with climate variability. To promote irrigation, the authors suggest the establishment of a subsidy and credit mechanism by the government. Factors that influenced adoption of efficient adaptation strategies to climate events must be taken into account for future adaptation policies in the Republic of Benin.

This study provided an overview of the perceptions and adaptations of rice farmers along the climatic gradient in the Republic of Benin. Therefore, the knowledge of the determining factors of the adaptation strategies used by rice farmers could be used in the setting up of effective climate change resilience policies in Benin.

The small-scale artisanal fishers in coastal Bangladesh are comparatively more vulnerable to climate risks than any other communities in Bangladesh. Based on practicality, this paper aims to explain the local level climate change perception, its impact and adaptation strategies of the fisher in southeast coastal villages in Bangladesh.

To achieve the above objective, this study used structural, semi-structured interviews and focus group discussion in two coastal communities, namely, at Salimpur in the Sitakund coast and Sarikait Sandwip Island, Bangladesh. It reviews and applies secondary data sources to compare and contrast the findings presented in this study.

Results show that the fishers perceived an increase in temperature, frequency of tropical cyclones and an increase in sea level. They also perceived a decrease in monsoon rainfall. Such changes impact the decreasing amount of fish in the Bay of Bengal and the fishers’ livelihood options. Analysing seasonal calendar of fishing, findings suggest that fishers’ well-being is highly associated with the amount of fish yield, rather than climatic stress, certain non-climatic factors (such as the governmental rules, less profit, bank erosion and commercial fishing) also affected their livelihood. The major adaptation strategies undertaken include, but are not limited to, installation of tube well or rainwater harvesting plant for safe drinking water, raising plinth of the house to cope with inundation and use of solar panel/biogas for electricity.

Despite experiencing social stress and extreme climatic events and disasters, the majority of the fishing community expressed that they would not change their profession in future. The research suggests implementing risk reduction strategies in the coastal region of Bangladesh that supports the small-scale fishers to sustain their livelihood despite climate change consequences.

The purpose of this study is to identify the most vulnerable households and districts in Northwest Ethiopia and help decision-makers in developing and prioritising effective adaptive strategies and actions.

A multi-scale analytical tool and hazard-generic socio-economic indicators were developed to identify and prioritise the most vulnerable households and districts in Northwest Ethiopia. Categorical principal component analysis with 36 indicators was used to develop weights for different indicators and construct a household intrinsic vulnerability index. Data were collected through key information interviews, focus group discussions and a household survey with 1,602 randomly selected households in three districts of Northwest Ethiopia.

Drawing on intrinsic vulnerability index computation, this study highlights that low levels of education, low access to climate information and credit services, long distance travelled to fetch water and frequent food shortages are the dominant factors contributing to high levels of intrinsic vulnerability at district level, while lack of livelihood support and income diversification are the key drivers of vulnerability at household level. The findings of this study further show that the majority of households (78.01%) falls within the very high to moderately high vulnerable category. Disaggregating the data according to agro-climatic zones highlights that the prevalence of high intrinsic vulnerability is most widespread in the lowland agro-climatic zone (82.64%), followed by the highland (81.97%) and midland zones (69.40%).

From a policy intervention vantage point, addressing the drivers of vulnerability provides a reliable approach to reduce the current vulnerability level and manage potential climate change-induced risks of a system. Specifically, reliable information on inherent vulnerability will assist policymakers in developing policies and prioritising actions aimed at reducing vulnerability and assisting in the rational distribution of resources among households at a local level.

This study contributes to the existing vulnerability literature by showing how hazard-generic socio-economic indicators in the vulnerability assessment adopted by the IPCC (2014) are important to identify drives of vulnerability which ultimately may feed into a more fundamental treatment of vulnerability.

Fewer researchers have investigated the climatic and economic drivers of land-use change simultaneously and the interplay between drivers. This paper aims to investigate the nonlinear and interaction effects of price and climate variables on the rice acreage in high-latitude regions of China.

This study applies a multivariate adaptive regression spline to characterize the effects of price and climate expectations on rice acreage in high-latitude regions of China from 1992 to 2017. Then, yield expectation is added into the model to investigate the mechanism of climate effects on rice area allocation.

The results of importance assessment suggest that rice price, climate and total agricultural area play an important role in rice area allocation, and the importance of temperature is always higher than that of precipitation, especially for minimum temperature. Based on the estimated hinge functions and coefficients, it is found that total agricultural area has strong nonlinear and interaction effects with climate and price as forms of third-order interaction. However, the order of interaction terms reduces to second order after absorbing the expected yield. Additionally, the marginal effects of driven factors are calculated at different quantiles. The total area shows a positive and increasing marginal effect with the increase of total area. But the positive impact of price on the rice area can only be observed when price reached 50% or higher quantiles. Climate variables also show strong nonlinear marginal effects, and most climatic effects would disappear or be weakened once absorbing the expected rice yield. Expected yield is an efficient mechanism to explain the correlation between crop area and climate variables, but the impact of minimum temperature cannot be completely modeled by the yield expectation.

To the best of the authors’ knowledge, this is the first study to examine the nonlinear response of land-use change to climate and economic in high-latitude regions of China using the machine learning method.

The farmers used several information sources to gather information about the climatic variability and modern agricultural practices to cope with climate change. The choice of adaptation strategies and the successful implication of adaptation strategies depend on accurate, timely information on the climate variability and precise technical details of adaptation strategies. By keeping the importance of climate information and agricultural extension information in the center, this study aims to conduct a micro-level evaluation of farmers’ choice of climate information, agriculture extension services and agricultural credit sources. This study’s primary objective is to understand how the different sources of climate information and agricultural extension influence farm household adaptation decisions.

This study has been conducted in three subs agro-climatic zone of the Middle Gangetic Plain region, which falls in India’s Bihar state. This paper has randomly selected seven districts from these three subs agro-climatic zone to collect the data. The analysis of this study is based on survey data collected from 700 farm households. This study has used descriptive statistics and a logistic regression model to assess the sources of climate information, agricultural extension and credit sources and how these sources influence farm households’ adaptation decisions.

The result of this study shows farmers are using different traditional (sharing experience, newspaper and radio), information and communication technology (mobile and TV) and institutional arrangements (agricultural officer and meteorological department) in the study area. The study’s finding identifies different farm households’ different sources and how these options farming farmers’ adaptation decisions. The study further revealed that institutional factors such as extension services and access to information on climate change increase the probability of adopting knowledge-intensive adaptation strategies such as soil conservation, water conservation, crop insurance and planting horticulture and vegetables.

The study has conducted a micro-level assessment of adaptation behavior at the local level to understand the factor influencing the adaptation decision. This study’s finding is useful in designing the appropriate policy framework for the farm household’s capacity building to enhance their technical skills and awareness toward the institutional arrangements.

This paper’s finding pointed out institutional arrangements’ requirement to improve adaptive capacity to make long-term strategic decisions to cope with climate change.

This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.