• Responses of carbon emissions to corruption across Chinese provinces

      Ren, Yi-Shuai; Ma, Chao-Qun; Apergis, Nicholas; Sharp, Basil; Hunan University, China; University of Auckland, New Zealand; University of Derby (Elsevier BV, 2021-03-19)
      In response to the recent growth of multitudes of theoretical literature analysing the corruption impact on the economy and environment, this paper subjects the corruption–carbon emission relationship in China to a detailed empirical examination through the autoregressive distributed lag modelling approach and panel quantile regressions. Based on panel data from Chinese provinces, spanning the period 1998–2016, this study explores the impact of long- and short-term corruption on per capita carbon emissions by considering the heterogeneous distribution of those emissions. The results document that corruption increases per capita carbon emissions in Chinese provinces in the short run, reducing per capita carbon emissions in the long run. Moreover, an increase in corruption leads to an increase in carbon emissions per capita in all quantiles, indicating that these emissions increase with corruption severity. The coefficients in low quantiles are slightly larger than those in high quantiles, indicating that corruption leads to more carbon emissions in provinces with lower per capita carbon emissions.
    • US partisan conflict uncertainty and oil prices

      Apergis, Nicholas; Hayat, Tasawar; Saeed, Tareq; University of Derby; King Abdulaziz, University, Saudi Arabia; Quaid-I-Azam University, Pakistan (Elsevier BV, 2021-01-13)
      This empirical study significantly contributes in building emerging literature by investigating the impact of US partisan conflict uncertainty on international oil prices. It models oil prices through non-linear Quantile Autoregressive Distributed Lag (QARDL) methods in order to consider potential (non-linear) asymmetric effects of partisan political uncertainty on oil prices. The empirical results clearly document the asymmetric (non-linear) impact of partisan conflict uncertainty on international oil prices, which has been in contrast to the linear case. The findings also expose that the transmission mechanism of partisan political uncertainty to oil prices is validated through the economic growth channel. The empirical findings contribute to existing research by assisting investors in the oil industry with risk identification, analysis, and mitigation. The results can assist in discovering the links between US political risk and oil markets, determining an important element of political risk factors facing investors who want to participate in the oil industry.