基于效率-公平原则的两阶段DEA网络模型铁路部门碳配额研究

An efficiency and equity principle based two-stage DEA network model driven carbon allowances for the national railroad sector

关键词:  数据包络分析(DEA)  碳排放  碳配额  铁路部门  分配

Key words:  data envelopment analysis (DEA)  carbon emission  carbon allowance  railway sector  allocation

摘要:

挖掘铁路行业的碳市场潜力，是提升管理效能，落实双碳战略的重要途径。文章通过核算2017— 2020年中国铁路部门碳排放，基于效率-公平耦合原则，构建了数据包络分析（DEA）网络模型驱动的铁路部门碳配额分配模型，提出面向2030年碳达峰目标的碳配额分配方案。结果显示：（1）2017—2020年铁路部门碳排放呈现先增加后降低趋势。其中，2017—2020年，碳排放先由6083.60×104 t增至6372.10× 104 t，增幅达4.74%；后在2020年降至5775.80×104 t。（2）碳排放整体呈现由客货周转量较大地区向周转量小地区递减的趋势，其中上海局碳排放贡献最大，2017—2020年碳排放总量为2218.68×104 t，青藏铁路公司碳排放贡献最小，2017—2020年碳排放总量为1070.74×104 t。（3） 兼顾效率与公平原则下，北京局、太原局、呼和浩特局及上海局分配的碳配额较多，分别为841.77×104 t、630.40×104 t、585.81×104 t 和 510.52×104 t；而青藏铁路公司、昆明局和哈尔滨局则分配的碳配额较少，分别为 301.23×104 t、 295.54×104 t和294.82×104 t。（4）北京局、太原局、呼和浩特局及西安局等铁路局的碳配额大于其碳排放预测值，其差值分别为342.57×104 t、280.69×104 t、224.35×104 t及132.84×104 t；而上海局、武汉局、广州局及南昌局等铁路局的碳配额小于其碳排放预测值，其差值分别为−210.17×104 t、−189.50×104 t、 −170.77×104 t和−158.63×104 t。

Abstract:

Background, aim, and scope Leveraging the rail sector’s carbon market potential is an important way to improve management efficiency and implement the “Carbon Peak and Carbon Neutrality” strategy. This study provides an efficiency and equity principle based two-stage of data envelopment analysis (DEA) network model driven carbon allowances for the national railroad sector, to propose an appropriate allocation scheme towards the 2030 carbon peak target. Materials and methods Based on the data from China Statistical Yearbook and China Energy Statistical Yearbook during the period of 2018—2021, this study reveals the structure and spatio-temporal distribution characteristics of carbon emissions regarding China’s railway sector from 2017 to 2020. A dynamic twostage carbon allowance allocation model is constructed to obtain the carbon allowances for each sub-bureau of the railway sector. Results The results show that between 2017 and 2020, carbon emissions from the railway sector had a trend of increase and then a trend of decrease. Among them, from 2017 to 2019, carbon emissions increased from 6083.60×10⁴ t to 6372.10×10⁴ t, an increase of 4.74%, and then decreased to 5775.80×10⁴ t in 2020. The carbon emissions show a decreasing trend from regions with high passenger and freight volumes to regions with low volumes. The Shanghai Bureau contributed the largest carbon emissions, with a total carbon emission of 2218.68×10⁴ t from 2017 to 2020, and the Qinghai-Xizang Bureau contributed the least carbon emissions, with a total carbon emission of 1070.74×10⁴ t from 2017 to 2020. Beijing Bureau, Taiyuan Bureau, Hohhot Bureau and Shanghai Bureau received the largest proportion of the carbon allowances, with 841.77×10⁴ t, 630.40×10⁴ t, 585.81×10⁴ t and 510.52×10⁴ t, respectively. However, Qinghai-Xizang Bureau, Kunming Bureau, and Harbin Bureau received the least carbon allowances, with only 301.23×10⁴ t, 295.54×10⁴ t and 294.82×10⁴ t, respectively. Discussion This study employs a two-stage allocation model that integrates the principles of fairness and efficiency, to reduce the carbon allowances for railway bureaus such as Shanghai Bureau and Guangzhou Bureau, which are located in economically developed regions and characterized by heavy tasks regarding passenger and freight transportation. Conversely, it increases the carbon allowances for railway bureaus such as Lanzhou Bureau and Qinghai-Xizang Bureau, which exhibit lower transportation turnover and smaller regional population density. This approach not only narrows the disparity in the allocation results but also facilitates emissions reduction among railway bureaus. Besides, the approach can better reveal the process relationship between the entire production process and its sub-stages. It also addresses the limitations of static allocation, where interactions among indicators can complicate optimal outcomes, ensuring that each railway bureau achieves the most efficient allocation.Conclusions Carbon emissions and carbon allowances exhibit a spatial decline, gradually decreasing from the eastern region to the western region and transitioning from resource-intensive areas to resource-poor areas. Recommendations and perspectives In the future, railway bureaus can develop targeted emissions reduction strategies based on the allocation of allowances. Combined with the allocation results, it is further expected to facilitate the design of the carbon pricing mechanism for railway bureaus, to promote the sector emissions reduction.