Assessing Representative CCUS Layouts for China’s Power Sector toward Carbon Neutrality

Abstract

China’s carbon neutrality target is building momentum for carbon capture, utilization, and storage (CCUS), by which the power sector may attain faster decarbonization in the short term. However, an overall CCUS pipeline network blueprint remains poorly understood. This study, for the first time, links the China TIMES model and ChinaCCUS Decision Support System 2.0 to assess representative CCUS layouts for the power sector toward carbon neutrality, with the level of deployment and the maximum transportation distance from emission sources to storage sites considered. The total length of the proposed layouts under the low, medium, and high levels of deployment are about 5100, 18,000, and 37,000 km, with the annum CO2 captures of 0.4, 1.1, and 1.7 Gt, respectively, whereas pipes of medium diameters (12, 16, and 20 in) account for the majority in all three plans. As the deployment rate increases, the layouts generally spread from Northeast, North, and Northwest China to East, South Central, and Southwest China. However, some local exceptions take place considering terrain areas that the pipeline passes through. In addition to engineering guidance, this assessment also illuminates the opportunity for improving the efficiency of CCUS based on carbon pricing.

China’s energy transitions for carbon neutrality: challenges and opportunities

“…There are several prominent changes in industrial CO 2 emissions mainly from cement, steel and chemicals sectors between 2020 and 2060, showing a rapidly decline trend [ 38 ]. Industrial CO 2 emissions decrease by 95% by 2060 due to electrification, energy efficiency improvements, hydrogen and CCUS [ 39 ] in industry. The decreasing trend of industrial CO 2 emissions is consistent with the trend of final energy consumption in China with decreasing coal and increasing electricity consumption.…”

Advances, challenges, and perspectives for CCUS source-sink matching models under carbon neutrality target

“…When a model was not assigned an original name in the published literature, its name can be determined by combining the associated source-sink matching approach, network characteristics, or the author's name with the addition of the study area. Thus, 16 models were renamed as follows: M1_SimCCS [18]; M2_CCSPD [19,20]; M3_SimCCS_TIME [21]; M4_InfraCCS_EUR [22]; M5_ChinaCCS_DSS [23,24]; M6_PNS [25]; M7_SCN_US [12,13]; M8_MARKAL_ IND [26]; M9_MILP_EUR (d'Amore et al, [9,10]); M10_MINLP_KOR [27]; M11_MILP_ESP [28]; M12_ ITEAM_CHN [14,29]; M13_SSM_WANG_CHN [30]; M14_SSM_FAN_CHN [11,15]; M15_C3IAM/GCOP (Wei et al, [17]); and M16_ChinaCCUS_DSS_2.0 [16] (Please refer to Supplementary Table 1 and following List of Abbreviations for the detailed information of the 16 types of CCUS source-sink matching models).…”

“…For example, the M15_C3IAM/GCOP model [17] proposed a global source-sink matching layout from the perspective of limiting the global temperature rise by 2 °C, targeting the final optimization goal that achieves the 92 Gt mitigation contribution of CCUS at a minimal cost. The M16_ChinaCCUS_DSS_ 2.0 model [16] assessed the effect of CCUS in the overall realization of the optimal route for China's carbon neutrality by connecting the CCUS source-sink matching model with the China TIMES model. This coupling could also provide solutions for studying the impact of technological competition on the layout of CCUS at a national scale.…”

“…Consequently, some important elements or limitations should be carefully considered, such as the industrywide carbon sources, the grid-level CO 2 injection rate capacity, the high-profit utilization option of enhanced oil recovery (EOR), the real geographic conditions, and the growing network in response to the changing mitigation demand over time [11][12][13][15][16][17]. Furthermore, the following two important aspects may have been overlooked: (i) an explicit mitigation target of CCUS driven by carbon neutrality, i.e., there is still a lack of indepth insights on how existing models can better connect demand-oriented source-sink matching to guide emerging capability targets; and (ii) the integration of models, such as the uncertainty of deployment led by technology competition, policies, public acceptance, risk assessment, negative-carbon technology system, and so on.…”

Reassessing the Effect of Low-Carbon City Policy in China: New Evidence From the Nighttime Light Data

“…Secondly, we use night light data to more accurately study the causal relationship between LCCP and carbon emissions. Due to the lack of city-level carbon emissions data, or the inaccurate measurement of carbon emissions, the literature can only conduct case studies on specific cities (Yu et al, 2019), or study the air pollution, building operations (Li K et al, 2022;Zhang et al, 2022), international trade (Li M et al, 2021;Liu et al, 2021), clean energy and energy efficiency (Sun et al, 2019;Zhang and Hanaoka, 2021), technological innovation and industrial development (Su et al, 2021;Tang et al, 2021), vegetation (Chen et al, 2021;Liao et al, 2021) and environmental efficiency of LCCP (Song et al, 2020). The Nighttime lighting data can not only provide a more accurate strategy for total city carbon emissions (Zhao et al, 2019;Li and Wang, 2022), but also let us more accurately assess the impact of LCCP on carbon emissions.…”