<b>Supplemental Data for </b><b>Fewer than 15% of coal power workers in China can easily shift to green jobs by 2060</b>

<b>The repository primarily contains the data files related to the publication entitled "Fewer than 15% of coal power workers in China can easily shift to green jobs by 2060"</b><b>. Certain datasets are directly accessible via the links provided in the Data and Code Availability section of the article and are therefore not duplicated within this repository. For any additional data not included, please feel free to contact us at </b><b>[e-mail]</b><b>, and we will provide it upon receiving a reasonable request.</b><b>Furthermore, the methodologies employed in the models utilized can be referenced in our previous publications. We are actively engaged in refining our models and are committed to progressively open-sourcing our model code in future research endeavors. We invite interested scholars to collaborate with us and encourage them to remain informed about forthcoming updates.</b><br><b>Supplemental Data S1:</b> This Excel document encompasses datasets pertaining to the probability of implementing solar and wind energy in the geographical areas where coal mining workers are located.<b>Supplemental Data S2:</b> This Excel document encompasses datasets pertaining to the probability of implementing solar and wind energy in the geographical areas where coal power workers are located.<b>Supplemental Data S3:</b><b> </b>This Excel document illustrates the geographic distribution of green jobs generated by solar and wind energy is presented, with the magnitude of coal power workers and renewable-energy-related green jobs aggregated at the city level.<b>Supplemental Data S4:</b><b> </b>This Excel document illustrates the geographic distribution of green jobs generated by solar and wind energy is presented, with the magnitude of coal power workers and renewable-energy-related green jobs aggregated at the province level.<b>Supplemental Data S5:</b><b> </b>This Excel document presents power generation by various technologies (coal, gas, nuclear, biomass, hydro, wind, solar PV, and storage) from 2020 to 2060 under different emission pathways.<b>Supplemental Data S6:</b><b> </b>This Excel document illustrates the distribution of coal power workers categorized by commuting distance across various provinces. The provinces are identified by their abbreviations, with the full names of the provinces available in the Supplemental Table titled "Province Names, Corresponding Abbreviations, and Regions within China."<b>Supplemental Data S7: </b>This Excel document records the average commuting distance per worker at the provincial and plant levels, respectively, under the PATH-M scenario from 2020 to 2060.<b>Supplemental Data S8:</b><b> </b>This Excel document provides a record of the proportion of coal power workers who are transitioning to green jobs across various coal power retirement pathways. It also presents the number of coal power workers who have successfully transitioned to green jobs, specifically under the OPT and Real scenarios, within each province as outlined in the PATH-M scenario.本仓库主要收录与题为《到2060年中国仅不足15%的煤电从业者可顺利转型至绿色岗位》的学术论文相关的数据文件。部分数据集可直接通过论文“数据与代码可用性”章节中提供的链接获取，因此本仓库未重复收录此类数据。如需获取未包含在本仓库中的额外数据，请致信至[e-mail]与我们联系，我们将在收到合理请求后提供对应数据。此外，本研究使用的模型方法可参考我们此前发表的学术成果。我们正积极优化模型，并计划在后续研究中逐步开源模型代码。诚邀相关领域学者与我们展开合作，也敬请关注后续更新。 补充数据S1：该Excel文档包含采煤从业者所在地理区域部署太阳能与风能的概率相关数据集。 补充数据S2：该Excel文档包含煤电从业者所在地理区域部署太阳能与风能的概率相关数据集。 补充数据S3：该Excel文档展示了太阳能与风能所创造的绿色岗位的地理分布情况，并将煤电从业者数量与可再生能源相关绿色岗位数量按地级市进行汇总统计。 补充数据S4：该Excel文档展示了太阳能与风能所创造的绿色岗位的地理分布情况，并将煤电从业者数量与可再生能源相关绿色岗位数量按省级行政区进行汇总统计。 补充数据S5：该Excel文档展示了不同排放路径下，2020年至2060年间各类技术（煤炭、天然气、核电、生物质能、水电、风电、光伏（solar PV）及储能）的发电量数据。 补充数据S6：该Excel文档展示了按通勤距离划分的各省煤电从业者分布情况。各省以简称标识，完整名称可参见补充表格《中国省级行政区全称、对应简称及所属区域》。 补充数据S7：该Excel文档记录了PATH-M情景下，2020年至2060年间各省及各电厂层面的从业者平均通勤距离数据。 补充数据S8：该Excel文档记录了不同煤电退役路径下，转型至绿色岗位的煤电从业者占比情况；同时展示了在PATH-M情景下，OPT与Real两种场景中，各省级行政区成功转型至绿色岗位的煤电从业者数量。