The literature reveals that linear models do not accurately represent the asymmetric relationship between economic growth and energy consumption (EC). To fill this gap, we examined the asymmetric relationship between EC and economic growth in a non-linear panel autoregressive distributed lag (ARDL) framework of 85 countries as a whole sample and of Brazil, Russia, India, China, and South Africa (BRICS), the Next Eleven, Big Four in Western Europe, Asia-Pacific region, Group of Seven, South Asian Association for Regional Cooperation (SAARC), Economic Cooperation Organization (ECO) and the Arab League as a sub-sample analysis from 1977 to 2014. A second generational unit root test has been applied to check the problem of cross-sectional dependency. Asymmetric co-integration was employed to analyse the co-integration between the variables of interest. Long-run and short-run estimates have been calculated using the non-linear panel ARDL method. Results indicate that positive shocks to energy use tend to have a growth-enhancing effect in ECO and the Next Eleven while in the rest of the regions, the effect is growth contraction. Moreover, economic recovery from a positive shock to energy use is the case in the Arab League, Asia-Pacific region, Group of Seven and in the whole sample. However, a negative shock to EC is observed in the Group of Seven, Asia Pacific region, Big Four in Western Europe and ECO, and the whole sample worsens the economic contraction. We can deduct from this study’s results that information on the asymmetric relationship between the subject variables is needed to design sound economic policy decisions across different economic regions.

This study examines the impact of Cohesion Policy, and institutional quality, on regional economic performance across NUTS2 regions in 27 EU member states from 2007 to 2022. Average per capita Cohesion Policy payments are much higher in less developed regions, showing the policy focus on supporting lagging areas. Furthermore, the analysis groups regions as urban, rural and intermediate at the NUTS2 level to reflect key territorial differences and better assess how Cohesion Policy impacts vary across distinct development challenges. However, very low institutional quality in these regions may limit the effectiveness of this financial support. Using a two‐step system generalised method of moments approach, the analysis finds that Cohesion Policy transfers have a positive impact on regional economic performance, with notable variation across regional types. The interaction between regional typologies and funding indicates that the policy is particularly effective in less developed, rural and intermediate regions, where structural disadvantages are more pronounced. Institutional quality plays a moderating role in this relationship and emerges as a key factor in enhancing Cohesion Policy effectiveness. To increase the impact of Cohesion Policy in lagging and rural regions, financial support should be complemented with efforts to improve institutional quality.

Problem is not well mentioned in the abstract and in rest of the paper. Introduction is general stories which have no link with the objective and problem of the study. Moreover problem of the study is either not mentioned or incorrectly mentioned in...

Nightlights data have emerged as a valuable proxy for economic activity, enabling gross domestic product (GDP) prediction at sub-national levels. The availability of district-level GDP plays a crucial role in understanding regional economic disparities and formulating targeted policy interventions. However, the choice between the Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS) nightlights data remains a critical decision in the context of accurate GDP prediction. This research article aims to address the question: ‘DMSP or VIIRS: Which nightlights data to use for the prediction of GDP in South Asian economies?’ The renowned DMSP nightlights data, despite being widely utilized, have been subject to concerns regarding calibration, top coding and blurring, which may lead to biased GDP predictions and spatial inequality estimates. On the other hand, the VIIRS lights data, a more reliable alternative, have not been fully explored by economists for GDP prediction. To achieve the objectives of this study, the predictive performance of DMSP and VIIRS nightlights data for national GDP is compared, and the suitability of each dataset for district-level GDP estimation is assessed. Additionally, the research examines the impact of both datasets in countries with distinct economic characteristics, such as agricultural and industrial dependence, as well as varying population densities. The findings reveal that VIIRS lights data exhibit superior predictive capabilities for the national GDP of South Asian economies compared to DMSP lights data. This advantage becomes more apparent when using DMSP nightlights data in more spatially aggregated units, such as sub-national administrative units. Nevertheless, this performance difference between the two datasets persists across agriculture-dependent countries, industry-dependent countries, low-population-density countries and high-population-density countries. Notably, our results corroborate Gibson’s observations that the limitations of using DMSP lights data are less evident when applied to more aggregate spatial units, particularly at the national level. By shedding light on the comparative performance of DMSP and VIIRS data, this study contributes to advancing the understanding of the complexities involved in harnessing remote sensing data for economic analysis at the sub-national level.