<b>Supplemental Data</b>

<b>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><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.<b>若需获取本数据集未包含的额外数据，请致信 [e-mail] 联系我方，合理请求将获对应数据支持。</b><b>此外，本研究所用模型的方法可参考我们已发表的前期成果。我方正积极优化模型，并承诺在未来的研究工作中逐步开源模型代码。诚邀相关领域学者与我们开展合作，并敬请关注后续更新。</b> <b>补充数据S1：</b>本Excel文件包含煤炭采矿工人（coal mining workers）所在地理区域的太阳能与风电部署概率相关数据集。<b>补充数据S2：</b>本Excel文件包含燃煤发电工人（coal power workers）所在地理区域的太阳能与风电部署概率相关数据集。<b>补充数据S3：</b>本Excel文件展示了太阳能与风电催生的绿色岗位（green jobs）的地理分布情况，其中燃煤发电工人与可再生能源相关绿色岗位（renewable-energy-related green jobs）的规模以城市为单位进行汇总统计。<b>补充数据S4：</b>本Excel文件展示了太阳能与风电催生的绿色岗位的地理分布情况，其中燃煤发电工人与可再生能源相关绿色岗位的规模以省份为单位进行汇总统计。<b>补充数据S5：</b>本Excel文件包含不同排放路径（emission pathways）下，2020年至2060年各类发电技术（煤炭、天然气、核电、生物质能、水电、风电、太阳能光伏（solar PV）及储能）的发电量数据。<b>补充数据S6：</b>本Excel文件展示了按通勤距离（commuting distance）分类的各省份燃煤发电工人分布情况。表格中省份以简称标注，省份全称可参见题为《中国各省名称、对应简称及所属区域》的补充附表。<b>补充数据S7：</b>本Excel文件记录了PATH-M情景下，2020年至2060年各省及各电厂层面的工人平均通勤距离。<b>补充数据S8：</b>本Excel文件记录了不同燃煤发电退役路径下，转型至绿色岗位的燃煤发电工人占比情况；同时展示了PATH-M情景框架下，各省份在OPT情景与Real情景中成功转型至绿色岗位的燃煤发电工人数量。