Climate change: north and south EU economies—an application of dynamic asymmetric panel data models

Abstract

The need for a cleaner environment and wealthier economies has been highly recognized by European Union (EU) policymakers of the last years, as evidenced by the creation of a plethora of laws and regulations for reducing carbon dioxide emissions while promoting the economic prosperity of EU countries. Indeed, many works have been done in this field, remarking on climate change’s impacts on economies and the need for determinant environmental policies inside the EU. This paper investigates the effect of climate change on economic growth using nonlinear dynamic panel methods for 15 countries of the EU in the period 1981–2019. Specifically, it is examined the impact of temperature, precipitation, and CO2 emissions on economic growth. So, autoregressive distributed lags (ARDL) methods were employed, overcoming cross-dependency and also considering linearity and nonlinearity. The results showed that economic growth has positive nonlinear relationship with long-run temperature, but in short-run they have a symmetric negative association. Moreover, precipitation has long-run negative and a short-run positive relationship with economic growth. However, when CO2 emissions are added, then model’s performance is decreased, and precipitation has a positive effect on economic growth, but all others, except from temperature increase, become insignificant. Finally, actions should be taken for more stable climate conditions and consistent environmental policies by EU countries. Graphical abstract

The empirical analysis of cereal production under the climate exchange and examining the effects of banks’ domestic credit on cereal production: Evidence from Turkey

“…In the model, CP shows cereal production (Metrics tons) (Pickson et al, 2020;Rehman et al, 2021;Chandio et al, 2023); CO2 refers to per capita CO2 emissions (metric tons) as an indicator of climate change (Adzawla et al, 2019;Adam & Drakos, 2022); The RF specifies the average annual rainfall (mm) as an indicator of climate change (Alagidede et al, 2016;Castro et al, 2020); TP shows the annual average temperature (Celsius) as an indicator of climate change (Jatuporn & Takeuchi, 2023); LCP refers to the cereal production area (Pickson et al, 2020;Chandio et al, 2023); DC is domestic credit to private sector by banks (% of GDP) as banks' domestic credit .…”

Crop-climate link in the southeastern USA: A case study on Oats and Sorghum

“…The present study has used a panel dataset, often called cross-sectional or longitudinal time series data that comprises data of every variable repeating across the time series [46]. The panel dataset has been widely utilized in past crop-climate studies [47,48,49,50]. A minimum 30 years timeframe is suggested to adequately depict climatic uctuations to the ner level [51], and the panel data model requires a continuous dataset [52].…”