Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints

Economic globalization is developing more rapidly than ever before. At the same time, economic growth is accompanied by energy consumption and carbon emissions, so it is particularly important to estimate, analyze and evaluate the economy accurately. We compared different nighttime light (NTL) index models with various constraint conditions and analyzed their relationships with economic parameters by linear correlation. In this study, three indices were selected, including original NTL, improved impervious surface index (IISI) and vegetation highlights nighttime-light index (VHNI). In the meantime, all indices were built in a linear regression relationship with gross domestic product (GDP), employed population and power consumption in southeast China. In addition, the correlation coefficient [Formula: see text] was used to represent fitting degree. Overall, comparing the regression relationships with GDP of the three indices, VHNI performed best with the value of [Formula: see text] at 0.8632. For the employed population and power consumption regression with these three indices, the maximum [Formula: see text] of VHNI are 0.8647 and 0.7824 respectively, which are also the best performances in the three indices. For each individual province, the VHNI perform better than NTL and IISI in GDP regression, too. When taking employment population as the regression object, VHNI performs best in Zhejiang and Anhui provinces, but not all provinces. Finally, for power consumption regression, the value of VHNI [Formula: see text] is better than NTL and IISI in every province except Hainan. The results show that, among the indices under different constraint conditions, the linear relationships between VHNI and GDP and power consumption are the strongest under vegetation constraint in southeast China. Therefore, VHNI index can be used for fitting analysis and prediction of economy and power consumption in the future.