A multi-criteria decision-making approach to vulnerability assessment of rural flooding in Khyber Pakhtunkhwa Province, Pakistan.

Assessment of rural regions' vulnerability to flooding is gaining prominence on a global scale. However, researchers are greatly undermined in their efforts to make a comprehensive assessment owing to the multidimensional and non-linear link between different indicators and flood risk. Thus, a multi-criteria decision-making (MCDM) approach is proposed to assess the multifaceted vulnerability of rural flooding in Khyber Pakhtunkhwa Province, Pakistan. This research presents a hybrid model for flood vulnerability assessment by combining TOPSIS and the entropy weight method. Households' vulnerability to flooding in rural areas is assessed through four components (social, economic, physical, and institutional) and twenty indicators. All indicator weights are derived using the entropy weight method. The TOPSIS method is then used to rank the selected research areas based on their flood vulnerability levels. The ranking results reveal that flood vulnerability is highest in the Nowshehra District, followed by the Charsadda, Peshawar, and D.I. Khan Districts. The weighting results show that physical vulnerability is the most important component, while location of household's house from the river source (< 1 km) is the key indicator for assessing flood vulnerability. A sensitivity analysis is provided to study the impact of indicator's weights on the comprehensive ranking results. The sensitivity results revealed that out of twenty indicators, fourteen indicators had the lowest sensitivity, three indicators were reported with low sensitivity while the other three were considered highly sensitive for flood vulnerability assessment. Our research has the potential to offer policymakers specific guidelines for lowering flood risk in flood-prone areas.

Assessing the roles of absorption capacity in technological spillovers and economic growth nexus.

The paper aims to empirically assess the effects of technological spillovers on economic growth and to examine the roles of host country absorptive capacity. The empirical analysis was carried out at the country level on a panel of five Asian countries covering the period from 1972 to 2018. As the variable of interest (technological spillovers) and mediator variable (absorptive capacity) are captured with a variety of indicators, hence two empirical models are estimated with different specifications. The study's findings indicate that technological spillovers through all three channels have a positive effect on economic and TFP growth. Touching on the role of absorptive capacity in technological spillovers and economic growth nexus, study findings reveal that the human capital of the sample countries has no significant role to absorbed imported technology in the growth process of the host country. However, the empirical indication illustrates that a country holding comparatively more domestic R&D expenditure yields the potential gain of technological spillovers in economic growth.

A spatial econometric analysis of convergence in global environmental performance.

This study intends to test the presence of ß-convergence in the global Environmental Performance (EP). For this purpose, spatial Green Solow model is used as the theoretical framework of the study. Data of 88 developed and developing countries for the periods 1978-2017 is used. The present study utilizes ecological footprint (EF) as a comprehensive Environmental Performance Indicator (EPI). For data analysis, spatial econometric techniques have been used. To explore the spatial dependence of EP, Moran's I statistic was used. For regression analysis, this study has made the use of Spatial Durbin Model (SDM). Findings of the study indicate that there is positive spatial autocorrelation in the EP of the countries which means countries with similar EP are clustered together. Results of SDM confirm the existence of ß-convergence in the global EP. Physical capital was found to degrade environment while break-even investment (BEI) was found to improve it.