碳交易机制下废弃物发电技术投资组合优化研究

doi:10.16381/j.cnki.issn1003-207x.2024.1357

Abstract

Abstract:

The construction and operation of waste-to-energy technology represents an important direction for future sustainable development. For large-scale waste management enterprises， optimizing waste-to-energy technology portfolios is an effective strategy to achieve long-term returns and reduce investment risks. The China Certified Emission Reduction （CCER） mechanism provides carbon reduction benefits for registered waste-to-energy projects， but carbon price fluctuations and related policy changes introduce additional risks to portfolios. The options with robust portfolio optimization are combined to construct a waste-to-energy technology portfolio model applicable to carbon trading mechanisms， aiming to address the selection and capital allocation optimization problems for different waste-to-energy technologies （waste incineration， agricultural and forestry biomass， biogas power generation， etc.）. The model can quantify the impact of parameter fluctuations in uncertain environments and improve the accuracy of investment return forecasts by evaluating the value of flexible decision-making. Compared with traditional portfolio models， the model proposed in this study is more suitable for investment decision optimization under conditions of high uncertainty. It focuses on analyzing the impact of carbon trading market changes on the investment portfolio， designs multiple investment scenarios based on different market and policy changes， and conducts empirical analysis using waste-to-energy projects in Shaanxi， China as a case study. The results indicate that changes in the carbon trading market have a significant impact on waste-to-energy technology portfolio. Waste incineration， due to its higher average returns， holds a dominant position in the portfolio. Agricultural and forestry biomass technology， due to the additional consideration of fuel price uncertainty， experiences a significant decrease in investment proportion when carbon price volatility increases. Lowering the carbon emission baseline weakens project profitability， while increasing carbon prices can substantially enhance portfolio value. Moderate increases in carbon price volatility can enhance portfolio value， but excessive volatility may prompt decision-makers to adopt more conservative strategies， limiting portfolio value growth.

Key words: waste-to-energy, carbon trading mechanism, robust portfolio optimization, real options, technology portfolio

CLC Number:

F272.3

Huanyue Chen,Junfei Hu,Sijun Bai. Optimization of Waste-to-Energy Technology Portfolios Under the Carbon Trading Mechanism[J]. Chinese Journal of Management Science, 2026, 34(7): 347-358.

Figures/Tables 8

参数	描述	农林生物质	沼气	垃圾焚烧
$E p 0$	市场电价（元/千瓦时）	0.33	0.33	0.33
$u E p$	电价漂移率	0.02	0.02	0.02
$φ E p$	电价波动率	0.01	0.01	0.01
$U i c 0$	单位投资成本（元/千瓦）	14,000	22,000	20,000
$u n U i c$	单位投资成本漂移率	-0.05	-0.024	-0.025
$φ n U i c$	单位投资成本波动率	0.15	0.12	0.8
$G e 0$	单位发电效率（千瓦时/千瓦）	4,500	7,000	5,300
$u n G e$	单位发电效率漂移率	0.06	0.04	0.02
$φ n G e$	单位发电效率波动率	0.04	0.02	0.02
$C P 0$	碳价（元/吨）	40	40	40
$u C P$	碳价漂移率	0.02	0.02	0.02
$φ C P$	碳价波动率	0.03	0.03	0.03
$F u 0$	燃料价格（元/千克）	0.3		-
$u F u$	燃料价格漂移率	0.012	-	-
$φ F u$	燃料价格波动率	0.03	-	-

参数	描述	农林生物质	沼气	垃圾焚烧
$U o m n$	单位运维成本（元/千瓦时）	0.13	0.51	0.38
$U f$	垃圾处理费（元/吨）	-	-	16.5
$T W$	处理垃圾的总量（吨/年）	-	-	365×1000
$I C n$	装机容量（兆瓦）	20	20	20
$F i t n$	上网电价（元/千瓦时）	0.75	0.58	0.65
$C R B a s e$	碳排放基准（克/千瓦时）	1586	1586	1586
$C R n W T E$	项目排放量（克/千瓦时）	1	1	1
$v n$	增值税率	0	0	减免50%
$c n$	企业所得税率	0	减免90%	减免90%
$P f$	单位发电燃料消耗（千克/千瓦时）	1.05	-	-
$π$	贴现率	0.08	0.08	0.08
$T$	投资期	10	10	10
$L$	运营期	25	25	25

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情境类别	具体情境模式	情境代码	参数设置
情境类别	具体情境模式	情境代码	碳价(元/吨)	碳排放基准(克/千瓦时)	碳价波动率
基准情境	当前碳交易市场的自然经济增长	S0	40	1586	0.03
碳价	在基准情境的基础上提高碳价	A1	80	1586	0.03
碳价	在基准情境的基础上提高碳价	A2	120	1586	0.03
碳排放基准	在基准情境基础上降低碳排放基准	B1	40	750	0.03
碳排放基准	在基准情境基础上降低碳排放基准	B2	40	0	0.03
碳价波动	在基准情境基础上提高碳价波动率	C1	40	1586	0.1
碳价波动	在基准情境基础上提高碳价波动率	C2	40	1586	0.2

情境设置	预期收益（万元）
情境设置	0	100	200	300	400	500	600	700	800
S0	208.2	208.2	208.2	306.6	411.2	-	-	-	-
A1	408.1	408.1	408.1	408.1	408.1	509.9	613.5	-	-
A2	417.3	417.3	417.3	417.3	427.3	529.7	608.8	731.6	813.3
B1	187.2	187.2	205.9	301.5	-	-	-	-	-
B2	151.3	151.3	207.9	-	-	-	-	-	-
C1	373.5	373.5	373.5	373.5	441.8	-	-	-	-
C2	338.3	338.3	338.3	338.3	430.8	-	-	-	-

情境设置	预期收益（万元）
情境设置	0	100	200	300	400	500	600	700	800
S0	588.8	588.8	588.8	625.2	811.9	-	-	-	-
A1	763.5	763.5	763.5	763.5	923.5	968.1	1248.7	-	-
A2	968.6	968.6	968.6	968.6	968.6	1037.2	1139.3	1304.3	1440.6
B1	512.2	512.2	528.8	793.3	-	-	-	-	-
B2	434.4	434.4	581.2	-	-	-	-	-	-
C1	801.7	801.7	801.7	801.7	871.6	-	-	-	-
C2	863.2	863.2	863.2	863.2	1152.9	-	-	-	-

Optimization of Waste-to-Energy Technology Portfolios Under the Carbon Trading Mechanism

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