Understanding climate change vulnerability, adaptation and risk perceptions at household level in Khyber Pakhtunkhwa, Pakistan

Abstract

Purpose This study aims to investigate risks associated with climate change vulnerability and in response the adaptation methods used by farming communities to reduce its negative impacts on agriculture in Pakistan. Design/methodology/approach The study used household survey method of data collection in Charsadda district of Khyber Pakhtunkhwa province, involving 116 randomly selected respondents. Findings Prevalent crops diseases, water scarcity, soil fertility loss and poor socio-economic conditions were main contributing factors of climate change vulnerability. The results further showed that changing crops type and cultivation pattern, improved seed varieties, planting shaded trees and the provision of excessive fertilizers are the measures adapted to improve agricultural productivity, which may reduce the climate change vulnerability at a household level. Research limitations/implications The major limitation of this study was the exclusion of women from the survey due to religious and cultural barriers of in Pashtun society, wherein women and men do not mingle. Practical implications Reducing climate change vulnerability and developing more effective adaptation techniques require assistance from the government. This help can be in the form of providing basic resources, such as access to good quality agricultural inputs, access to information and extension services on climate change adaptation and modern technologies. Consultation with other key stakeholder is also required to create awareness and to build the capacity of the locals toward reducing climate change vulnerability and facilitating timely and effective adaptation. Originality/value This original research work provides evidence about farm-level vulnerability, adaptation strategies and risk perceptions on dealing with climate-change-induced natural disasters in Pakistan. This paper enriches existing knowledge of climate change vulnerability and adaptation in this resource-limited country so that effective measures can be taken to reduce vulnerability of farming communities, and enhance their adaptive capability.

What Defines Livelihood Vulnerability in Rural Semi-Arid Areas? Evidence from Pakistan

“…This research finds that most farmers are already adjusting their livelihoods to climate impacts. These adjustments include short-term, distress responses known as coping strategies as well as long-term modifications in their livelihood activities that can reduce their risk to climate impacts in the future (Khan et al 2011;Ullah et al 2018). …”

Trend in Extreme Precipitation Indices Based on Long Term In Situ Precipitation Records over Pakistan

“…The robust trend analysis of historical P extremes plays a key role in regional climate assessment, water resource management, and sustainable agricultural practices [16,17]. Moreover, the information obtained from such analysis is crucial in devising a compressive plan for future climate change adaptation and mitigation strategies at regional and local scales [18,19].…”

“…As a developing country, Pakistan needs advanced knowledge, innovative ideas, and a better understanding of the P extremes and their effects on our ecosystem and different sectors of the society [19,58]. Therefore, it is critical to assess the long-term spatiotemporal variations in P extremes and proposed useful recommendations for climate change adaptation and disaster risk reduction in the region.…”

Effects of Climate Finance on Risk Appraisal: A Study in the Southwestern Coast of Bangladesh

“…Furthermore, awareness is not sufficient for households to engage in adaptation measures as the households were extremely poor and lacked adequate resources to adapt. In fact, households, which intend to reduce the risks associated with climate change and have the resources or access to resources needed to make the appropriate changes, are generally more resilient and have a greater capacity to adapt [37]. Information variables can increase or decrease risk appraisals.…”