ALBERT

Description: Purpose The purpose of this paper is to understand local perceptions on climate change and its impacts on biodiversity, rangeland, agriculture and human health. Design/methodology/approach A household survey with 300 interviewees and focus group discussions with key stakeholders were conducted and validated at two steps, using the climate data from the nearest weather stations and reviewing literatures, to correlate the local perceptions on climate change and its impacts. Findings Majority of the respondents reported an increase in temperature and change in the precipitation pattern with increased hazardous incidences such as floods, avalanches and landslides. Climate change directly impacted plant distribution, species composition, disease and pest infestation, forage availability, agricultural productivity and human health risks related to infectious vector-borne diseases. Research limitations/implications Because of the remoteness and difficult terrain, there are insufficient local weather stations in the mountains providing inadequate scientific data, thus requiring extrapolation from nearest stations for long-term climate data monitoring. Practical implications The research findings recommend taking immediate actions to develop local climate change adaptation strategies through a participatory approach that would enable local communities to strengthen their adaptive capacity and resilience. Social implications Local knowledge-based perceptions on climate change and its impacts on social, ecological and economic sectors could help scientists, practitioners and policymakers to understand the ground reality and respond accordingly through effective planning and implementing adaptive measures including policy formulation. Originality/value This research focuses on combining local knowledge-based perceptions and climate science to elaborate the impacts of climate change in a localised context in Rakaposhi Valley in Karakoram Mountains of Pakistan.

Description: PurposeThis paper aims to examine the policy environments, institutional arrangements and practical implementation of some initiatives undertaken by the Government of Cameroon, together with some relevant stakeholders, in addressing climate change mitigation and adaptation at various levels in the country, which are prerequisites to promote synergistic ways of addressing climate change mitigation and adaptation.Design/methodology/approachUsing a qualitative approach to data collection, the paper draws upon information collected from relevant literature and interviews with 18 key country resource personnel.FindingsResults revealed that most reviewed policies/programs/strategies do not mention “climate change” explicitly but propose some activities which indirectly address it. Interaction is fair within the government ministries but weak between these ministries and other institutions. Inadequate financial resources are being opined as the most important challenge stakeholders are (and would continue) facing as a result of adopting integrated approaches to climate change. Other challenges include inadequate coordination, insufficient sensitization and capacity building, ineffective implementation, inadequate compliance, lack of proper transparency and inadequate public participation. To redress the aforementioned constraints and challenges, the paper concludes by outlining a number of recommendations for policy design.Originality/valueThe following recommendations were made: create a national technical committee to oversee and provide scientific guidance to the government on synergistic approaches; promote private sector investment and sponsorship on synergistic approaches; create local awareness, etc. It is important to underscore that minimal studies have been conducted to analyze multi-stakeholder perspectives on synergies between climate change mitigation and adaptation in Cameroon. This study attempts to bridge this major gap.

Description: Purpose This study aims to exploit the abundance of solar energy resources for socioeconomic development in the semi -arid Northeastern Brazil as a potent adaptation tool to global climate change. It points out a set of conjuncture factors that allow us to foresee a new paradigm of sustainable development for the region by transforming the sun’s radiant energy into electricity through distributed photovoltaic generation. The new paradigm, as presented in this essay, has the transformative potential to free the region from past regional development dogma, which was dependent on the scarce water resource, and the marginal and predatory use of its Caatinga Biome. Design/methodology/approach The research uses a pre ante design, following the procedures of scenario building, as an adaptation mechanism to climate change in the sector of energy generation and socioeconomic inclusion. Findings The scenarios of socioeconomic resilience to climate change based on the abundance of solar radiation, rather than the scarcity of water, demonstrates its potential as a global adaptation paradigm to climate change. Research limitations/implications The developments proposed are dependent on federal legislation changes, allowing the small producer to be remunerated by the energy produced. Practical implications The proposed smart grid photovoltaic generation program increases the country's resiliency to the effect of droughts and climate change. Social implications As proposed, the program allows for the reversion of a pattern of long term poverty in semi-arid Northeast Brazil. Originality/value The exploitation of the characteristics of abundance of the semiarid climate, i.e. its very condition of semi-aridity with abundant solar radiation, is itself an advantage factor toward adaption to unforeseen drought events. Extensive previous research has focused on weighting and monitoring drought i.e. the paradigm of scarcity. The interplay between exploiting Northeast Brazil’s abundant factors and climate change adaptation, especially at the small farmer levels constitutes a discovery never before contemplated.

Description: Purpose Groundwater is an important source of water supply in arid and semi-arid areas. The purpose of this study is to predict the impact of climate change on groundwater recharge in an arid environment in Ilam Province, west of Iran. Design/methodology/approach A three-dimensional transient groundwater flow model (modular finite difference groundwater FLOW model: MODFLOW) was used to simulate the impacts of three climate scenarios (i.e. an average of a long-term rainfall, predicted rainfall in 2015-2030 and three years moving average rainfall) on groundwater recharge and groundwater levels. Various climate scenarios in Long Ashton Research Station Weather Generator were applied to predict weather data. Findings HadCM3 climatic model and A2 emission scenario were selected as the best methods for weather data generation. Based on the results of these models, annual precipitation will decrease by 3 per cent during 2015-2030. For three emission scenarios, i.e. an average of a long-term rainfall, predicted rainfall in 2015-2030 and three years moving average rainfall, precipitation in 2030 is estimated to be 265, 257 and 247 mm, respectively. For the studied aquifer, predicted recharge will decrease compared to recharge calculated based on the average of long-term rainfall. Originality/value The decline of groundwater level in the study area was 11.45 m during the past 24 years or 0.48 m/year. Annual groundwater depletion should increase to 0.75 m in the coming 16 years via climate change. Climate change adaptation policies in the basin should include changing the crop type, as well as water productivity and irrigation efficiency enhancement at the farm and regional scales.

Description: PurposeThe development of models that allows the evaluation and prediction of erosion processes is an important tool for the management and planning of coastal systems. Mangrove forests systems are under threat by the impacts of erosion, which is also intensified by human activity (and aggravated in the scenarios of global warming and climate change). The purpose of this paper is to develop a model of geographic information systems (GIS) that can be used for any estuary area, but it can also be used for mangroves.Design/methodology/approachThis paper uses georeferentiation which is defined as a set of parameters that best characterize the mangrove areas: elevation (m); geomorphology; geology; land cover; anthropogenic activities; distance to the coastline (m) and maximum tidal range (m). Three different methods are used to combine the various vulnerability parameters, namely, DRASTIC index, analytical hierarchy process (AHP) and square root of the geometric mean.FindingsThe three approaches presented in this work show different types evaluating vulnerability to erosion, highlighting a stronger overvaluation of the areas presented with a high vulnerability, through the use of DRASTIC index when compared with two other approaches. The use of the AHP shows similarity to the square root of the geometric mean model, but the AHP also presents a higher percentage of vulnerable areas classified as having medium to very high vulnerability. On the other hand, the use of square root of the geometric mean led to a higher percentage of areas classified as having low and very low vulnerability.Research limitations/implicationsThese three qualitative models, based on a cognitive approach, using the set of parameters defined in this research, are a good tool for the spatial distribution of erosion in different mangroves in the world.Originality/valueGlobal warming and climate change scenarios require adaptation and mitigation options supported by science-based strategies and solutions.

Description: PurposeThis paper aims to investigate farmers’ vulnerability to climate change and variability in the northern region of Ghana.Design/methodology/approachThe study assessed the vulnerability of male-headed and female-headed farming households to climate change and variability by using the livelihood vulnerability index (LVI) and tested for significant difference in their vulnerability levels by applying independent two-sample-student’st-test based on gender by using a sample of 210 smallholder farming households.FindingsThe results revealed a significant difference in the vulnerability levels of female-headed and male-headed farming households. Female–headed households were more vulnerable to livelihood strategies, socio-demographic profile, social networks, water and food major components of the LVI, whereas male-headed households were more vulnerable to health. The vulnerability indices revealed that female–headed households were more sensitive to the impact of climate change and variability. However, female-headed households have the least adaptive capacities. In all, female-headed farming households are more vulnerable to climate change and variability than male-headed farming households.Research limitations/implicationsThe study recommends that female-headed households should be given priority in both on-going and new intervention projects in climate change and agriculture by empowering them through financial resource support to venture into other income-generating activities. This would enable them to diversify their sources of livelihoods to boost their resilience to climate change and variability.Originality/valueThis is the first study that examined the gender dimension of vulnerability of smallholder farmers in Ghana by using the livelihood vulnerability framework. Female subordination in northern region of Ghana has been profound to warrant a study on gender dimension in relation to climate change and variability, especially as it is a semi-arid region with unpredictable climatic conditions. This research revealed the comparative vulnerability of male- and female-headed households to climate change and variability.

Description: Purpose Perceptions of climate change and its threats to rural communities are among major challenges faced by scientists around the world. A few studies prove that these communities are aware of change in climatic conditions and their impacts on people’s livelihoods. Climate change is usually perceived as increasing warming days, erratic rainfall patterns, ecological variability, biological change and their adverse effects on human beings. This study aims to assess Ga-Dikgale community’s perceptions on climate change and variability. Design/methodology/approach A qualitative research method was adopted and community members of age 60 and above in GaDikgale community were purposively selected as participants in the study. Data were collected through in-depth interviews, and thematic content analysis was used to analyse data. Findings The study found that the community perceives climate change and climate variability based on changes in temperature patterns, erratic rainfall patterns, seasonal change, depletion of biodiversity, decline in subsistence crop production, change in water quality and cessation of cultural activities. Originality/value The study concludes that community’s perceptions of climate change are largely centred on variations in temperature and rainfall patterns. It has been established that knowledge of climate change in rural communities is of paramount importance in as far as adaptation to climate hazards is concerned.

Description: Purpose Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance. Design/methodology/approach This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix. Findings The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications. Originality/value This paper provides an insight into the current state and the future changes in carbon emissions.

Description: Purpose A significant number of studies have been conducted to analyze and understand the relationship between gas emissions and global temperature using conventional statistical approaches. However, these techniques follow assumptions of probabilistic modeling, where results can be associated with large errors. Furthermore, such traditional techniques cannot be applied to imprecise data. The purpose of this paper is to avoid strict assumptions when studying the complex relationships between variables by using the three innovative, up-to-date, statistical modeling tools: adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy time series models. Design/methodology/approach These three approaches enabled us to effectively represent the relationship between global carbon dioxide (CO2) emissions from the energy sector (oil, gas and coal) and the average global temperature increase. Temperature was used in this study (1900-2012). Investigations were conducted into the predictive power and performance of different fuzzy techniques against conventional methods and among the fuzzy techniques themselves. Findings A performance comparison of the ANFIS model against conventional techniques showed that the root means square error (RMSE) of ANFIS and conventional techniques were found to be 0.1157 and 0.1915, respectively. On the other hand, the correlation coefficients of ANN and the conventional technique were computed to be 0.93 and 0.69, respectively. Furthermore, the fuzzy-based time series analysis of CO2 emissions and average global temperature using three fuzzy time series modeling techniques (Singh, Abbasov–Mamedova and NFTS) showed that the RMSE of fuzzy and conventional time series models were 110.51 and 1237.10, respectively. Social implications The paper provides more awareness about fuzzy techniques application in CO2 emissions studies. Originality/value These techniques can be extended to other models to assess the impact of CO2 emission from other sectors.