S11 Fig -

Locations with March-April mean DOD above (a) 0.2, (c) 0.3, and (e) 0.4 in 2014 using MODIS and the spatial distribution of the population affected by DOD above (b) 0.2, (d) 0.3, and (f) 0.4 in 2014. (TIF)</p

S4 Fig -

Locations with March-April mean DOD above (a) 0.2, (c) 0.3, and (e) 0.4 in 2007 using MODIS and the spatial distribution of the population affected by DOD above (b) 0.2, (d) 0.3, and (f) 0.4 in 2007. (TIF)</p

DataSheet_1_Offshore wind power policies and green total factor productivity: empirical evidence from coastal China.pdf

IntroductionGreen and high-quality development requires the transformation and upgrading the energy structure. As a clean and efficient new energy, the development of offshore wind power is related to the achievement of green development and the realization of the dual carbon goals.MethodsBased on the perspective of green total factor production, this study aims to explore the impact of offshore wind power policies (OWPPs) on green and high-quality development. Taking 11 coastal areas of China from 2004 to 2020 as samples, this paper empirically tested the impact of OWPPs on green total factor productivity (GTFP) by using propensity score matching difference-in-differences method (PSM-DID).Results and discussionThe results show that OWPPs have a significant positive impact on GTFP. The robustness test further verifies the results, and the provincial difference is significant. By stimulating technological innovation and reducing energy intensity, OWPPs have improved GTFP, but increasing marketization level is a long way off.</p

The population (in a million) affected by March-April mean DOD > 0.3 in each province from 2003 to 2020.

The population (in a million) affected by March-April mean DOD > 0.3 in each province from 2003 to 2020.</p

S9 Fig -

Locations with March-April mean DOD above (a) 0.2, (c) 0.3, and (e) 0.4 in 2012 using MODIS and the spatial distribution of the population affected by DOD above (b) 0.2, (d) 0.3, and (f) 0.4 in 2012. (TIF)</p

S12 Fig -

Locations with March-April mean DOD above (a) 0.2, (c) 0.3, and (e) 0.4 in 2015 using MODIS and the spatial distribution of the population affected by DOD above (b) 0.2, (d) 0.3, and (f) 0.4 in 2015. (TIF)</p

Fig 2 -

(a) The population affected by March-April mean DOD above 0.2 (blue), above 0.3 (red), and above 0.4 (yellow) from 2003 to 2020 estimated using MODIS. The y-axis is on a log scale. (b) The population affected by DOD above 0.2 (POP_DOD_0.2), the population affected by DOD above 0.3 (POP_DOD_0.3), and the population affected by DOD above 0.4 (POP_DOD_0.4) as a function of DOD. Straight lines are the linear regression fit. POP_DOD_0.2 = 1.28×109DOD - 1.44×108 (r2 = 0.73), POP_DOD_0.3 = 5.11×108DOD - 5.39×107 (r2 = 0.74), POP_DOD_0.4 = 2.29×108DOD - 2.25×107 (r2 = 0.76).</p

S8 Fig -

Locations with March-April mean DOD above (a) 0.2, (c) 0.3, and (e) 0.4 in 2011 using MODIS and the spatial distribution of the population affected by DOD above (b) 0.2, (d) 0.3, and (f) 0.4 in 2011. (TIF)</p

Fig 1 -

(a) March-April mean DOD at 1° by 1° over China from 2003 to 2020 using MODIS daily DOD. The gray boxes are the major dust source regions of the Taklamakan Desert (74.5 to 90.5°E, 34.5 to 42.5°N) and the Gobi Desert (98.5 to 110.5°E, 35.5 to 42.5°N). (b) March-April mean DOD over China from 2003 to 2020 using MODIS (solid line) and the total number of dust events in March and April in China (dashed line) reported by the Annual Book of Meteorological Disasters in China (2019).</p

The population (in a million) affected by March-April mean DOD > 0.2 in each province from 2003 to 2020.

The population (in a million) affected by March-April mean DOD > 0.2 in each province from 2003 to 2020.</p