减污降碳协同视角下沿海制造业发达地区产业结构调整路径研究

吕一铮; 曹晨玥; 田金平; 陈吕军

doi:10.13198/j.issn.1001-6929.2022.07.13

减污降碳协同视角下沿海制造业发达地区产业结构调整路径研究

doi: 10.13198/j.issn.1001-6929.2022.07.13

吕一铮^1,,
曹晨玥¹,
田金平^{1, 2, ,},
陈吕军^{1, 2}

1.
清华大学环境学院，北京　100084
2.
清华大学生态文明研究中心，北京　100084

基金项目: 国家社会科学基金重大项目(No.18ZDA046)；国家自然科学基金项目(No.41971267)

详细信息

作者简介:
吕一铮（1996-），男，浙江绍兴人，lvyz19@mails.tsinghua.edu.cn

通讯作者:
田金平(1974-)，男，甘肃武威人，研究员，博士，主要从事产业生态学、生态工业园区相关研究，tianjp@tsinghua.edu.cn

中图分类号: X321；F205
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出版历程
- 收稿日期: 2022-05-25
- 修回日期: 2022-07-05

Industrial Structure Adjustment Path in Coastal Areas with Developed Manufacturing Industries from Perspective of Synergistic Reduction of Pollutants and CO₂ Emissions

LÜ Yizheng^1
,,
CAO Chenyue¹,
TIAN Jinping^{1, 2
, ,},
CHEN Lüjun^{1, 2}

1.
School of Environment, Tsinghua University, Beijing 100084, China
2.
Center for Ecological Civilization, Tsinghua University, Beijing 100084, China

Funds: National Social Science Foundation of China (No.18ZDA046)；National Natural Science Foundation of China (No.41971267)

摘要

摘要: 碳达峰碳中和是我国的重大战略决策，对推进产业转型升级和绿色发展具有重要意义. 实现经济增长与资源能源消耗、污染物和碳排放的总量与强度双控制，是推进“双碳”目标的重要支撑. 我国沿海地区制造业发达，污染物和碳排放量较大，寻找减污降碳协同增效路径对区域绿色转型具有重大现实意义. 本文以浙江省宁波市为对象，对全部经济门类的产业结构开展实证研究，运用多准则决策模型和情景分析法，以能源、水资源、4种主要污染物(化学需氧量、氨氮、二氧化硫、氮氧化物)和二氧化碳为约束条件建立了产业结构优化调整模型，将各产业增加值占比的变化程度作为决策变量，筛选出产业结构调整平稳、减排幅度大的调整方案. 制造业作为宁波市经济发展的主体，贡献了较高比例的污碳排放和能源资源消耗. 4.5%、5.5%、6.5%三种年均经济增速情景下宁波市通过产业结构调整实现减污降碳协同增效的潜力分析显示，2020—2030年预期可实现累计97%的经济增长，且能满足区域资源环境的约束限制. 面向2030年提出宁波市产业结构优化调整路径，建议严格控制高排放制造业的准入门槛，提升第一产业和采矿业的资源能源利用效率，推进电力、热力的生产与供应业等存量行业的减污降碳，鼓励发展高附加值的第三产业和循环经济产业.
- 产业结构调整 /
- 减污降碳协同增效 /
- 多准则决策 /
- 宁波市
Abstract: Carbon dioxide peaking and carbon neutrality are China's grand strategic goals which are of great significance for promoting the eco-transformation and green development of the manufacturing industry. Achieving the decoupling of economic growth from resource-energy consumption and pollutants-carbon dioxide emissions will underpin the goals substantially. Driven by the manufacturing industry, China′s coastal regions are well developed, but also have serious pollutants and carbon dioxide emissions. Thus, synergizing the reduction of pollutants and carbon dioxide emissions has great practical significance for regional eco-transformation. Taking Ningbo City in Zhejiang Province as a case study, this article conducts empirical research on the industrial structure adjustment of two-digital level industries by establishing a multi-criteria decision-making model under different scenarios. The model takes energy, four major pollutants (water resources, chemical oxygen demand, ammonia nitrogen, sulfur dioxide, nitrogen oxide), and carbon dioxide as constraints, and takes the degree of change in the proportion of the added value of each industry as the decision-making variable, and finally proposes the adjustment scheme with a relatively reasonable industrial structure variation and the largest emissions reduction. The manufacturing industries, as the main body of Ningbo′s economic development, contribute to a large part of pollutants and carbon dioxide emissions, and consume a lot of energy and water resources. This paper explores the potential of Ningbo City to synergize the pollutants and carbon dioxide reduction through industrial structure adjustment under three annual economic growth rates of 4.5%, 5.5% and 6.5%. The results show that Ningbo City can achieve a maximum cumulative economic growth of 97% from 2020 to 2030 while achieving the goal of pollutants and carbon dioxide emissions reduction. This paper puts forward suggestions for Ningbo City to facilitate industrial structure adjustment targeting the year 2030, including controlling the entry threshold for emission-intensive manufacturing industries, improving resource and energy productivity in the primary sector and mining industry, reducing pollutants and carbon dioxide emissions of electricity and heat production and supply industries further, and encouraging the development of high value-added tertiary industries and circular economy industry.
- industrial structure adjustment /
- synergistic reduction of pollutants and carbon dioxide emissions /
- multi-criteria decision making /
- Ningbo City

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图 1 区域产业结构优化调整模型框架示意

注：BAU-1和BAU-2指两类基准情景，ISA指产业结构调整情景.

Figure 1. Schematic diagram of the frame of the regional industrial structure adjustment model

下载: 全尺寸图片幻灯片

图 2 2014—2019年宁波市工业行业4种污染物排放强度拟合结果

Figure 2. Fitting curves of emission intensity of pollutants in industrial sectors from 2014 to 2019 of the Ningbo City

下载: 全尺寸图片幻灯片

图 3 宁波市两类基准情景和三类产业结构调整情景的产业结构

注：图中各部分外圈黑色数字为行业编号，纵向红色数字为对应行业的行业增加值占比(%).

Figure 3. Industrial structure in different industrial structure adjustment scenarios in the Ningbo City

下载: 全尺寸图片幻灯片

表 1 模型中使用的各类参数及其定义

Table 1. Definitions of key parameters used in the model

参数	定义
n	行业分类总数
h	基准年至目标年的时间跨度
${{\text{iav} _ {i} }}^{\text{0(t)} }$	序号为i的行业基准年(目标年)的产业增加值(10⁴元)
$ {\text{iav}}^{\text{0(t)}} $	全部行业基准年(目标年)的总产业增加值，即经济总量(10⁴元)
iav	目标设定的全部行业目标年的总产业增加值，即目标经济总量(10⁴元)
$ {\text{IAV}}^{\text{0(t)}} $	基准年(目标年)的产业增加值列向量，其中第i行的元素为${\mathrm{i}\mathrm{a}{\text{v} }_{ {i} }}^{\text{0(t)} }$
${ {s}_ {i} }^{\text{0(t)} }$	基准年(目标年)序号为i的行业的产业增加值占经济总量的比例(％)
g	总产业增加值的年均增长率(%)
q	污染物指标，包括化学需氧量(COD)、氨氮、二氧化硫(SO₂)和氮氧化物(NO_x)
${{C}_{ {i,q}} }^{\text{0(t)} }$	序号为i的行业在基准年(目标年)的污染物指标q的排放/消费总量(t)
${{\text{C} }_{ {q} }}^{\text{0(t)} }$	基准年(目标年)污染物指标q的全部行业排放/消费总量(t)
${{ {\rho } }_{ {i,q} }}^{\text{0(t)} }$	序号为i的行业在基准年(目标年)的指标q的排放/消费强度〔(t/(10⁴元)〕
${{\rho }_{ {q} }}^{\text{0(t)} }$	基准年(目标年)指标q的排放强度行向量，其中第i列元素为${{\rho }_{i,q}}^{\text{0(t)} }$
I	单位矩阵，主对角线上的元素均为1，其余元素为0
${{d} }_{{i,q} }$	基准年至目标年间序号为i的行业污染物指标q排放/消费强度的减少比例(%)
${{d} }_{{q} }$	${n}\times {n}$对角矩阵，其中对角线上的第i个元素为${{d} }_{{i,q} }$
${{K}_{i}}^{\mathrm{B}\mathrm{A}\mathrm{U} }$	产业结构调整因子，序号为i的行业目标年产业增加值占比相对于基准情景的倍数
T	用于反映产业结构变动方式是否平稳
Score	用于反映各类污染物的减排幅度与资源消费的减少幅度
[l,m]	${{K}_{i}}^{\mathrm{B}\mathrm{A}\mathrm{U} }$的取值范围

下载: 导出CSV

表 2 2014—2019年宁波市各项经济环境数据

Table 2. Economic and environmental performance of Ningbo City from 2014 to 2019

年份	全行业能源消费总量/ (以标准煤计)/(10⁴ t)	全行业万元增加值能耗/ (以标准煤计)/[kg/(10⁴ 元)]	全行业用水量/ (10⁴ t)	规模以上工业碳排放量/(10⁴ t)	全行业碳排放量/ (10⁴ t)
2014	3 112.02	511.99	60 278.28	17 449.36	18 345.51
2015	3 257.13	496.3	59 511.34	16 857.46	17 779.10
2016	3 464.33	492.91	61 690.20	16 590.99	17 522.99
2017	3 602.24	476.7	64 797.20	18 313.71	19 219.36
2018	3 640.05	418.07	65 735.74	17 716.00	18 509.76
2019	3 792.49	405.08	64 905.53	17 991.60	18 759.62

下载: 导出CSV

表 3 模型相关参数计算结果

Table 3. Results of key parameters used in the model

指标		2019年	2030年目标值	情景
指标		2019年	2030年目标值	BAU-1	BAU-2	ISA-1	ISA-2	ISA-3
工业增加值/(10⁸元)		9 362	—	16 872	16 872	15 209	16 956	18 879
CO₂	排放量/(10⁴ t)	18 760	19 219.36	28 202	17 146	13 637	16 386	16 800
CO₂	相对目标值的比例/%	—	—	46.74	−10.79	−29.05	−14.74	−12.59
COD	排放量/t	6 584	6 057	4832	3 496	2 883	3 113	3573
COD	相对目标值的比例/%	—	—	−20.22	−42.28	−52.41	−48.60	−41.02
氨氮	排放量/t	264	237	101	73	61	67	76
氨氮	相对目标值的比例/%	—	—	−57.42	−69.31	−74.32	−71.88	−68.08
SO₂	排放量/t	13 458	11 439	5 648	3 996	3 400	3 807	4 246
SO₂	相对目标值的比例/%	—	—	−50.63	−65.07	−70.28	−66.72	−62.88
NO_x	排放量/t	29 137	24 767	13 877	8 909	7 227	8 216	8 940
NO_x	相对目标值的比例/%	—	—	−43.97	−64.03	−70.82	−66.83	−63.90
水资源	消费量/(10⁴ t)	69 961	57 311	108 591	67845	51 972	57 311	64 341
水资源	相对目标值的比例/%	—	—	67.31	4.53	−16.12	−11.70	−0.87
能源	消费量(以标准煤计)/(10⁴ t)	3 793	5 078	6 025	4 890	4 106	4 637	5 078
能源	相对目标值的比例/%	—	—	29.34	4.97	−2.13	−0.45	−0.40

下载: 导出CSV

表 4 ISA-2情景下的敏感性分析结果

Table 4. Sensitivity analysis of the ISA-2 scenario

情景	${{K}_{i}}^{\mathrm{B}\mathrm{A}\mathrm{U}\text{-}2} < 1$ 的行业	${{K}_{i}}^{\mathrm{B}\mathrm{A}\mathrm{U}\text{-}2} > 1$ 的行业	T
ISA-2	行业1、8、9、12	行业26	1.3
目标年水资源消费强度降幅为基准情景的1.1倍	行业1、9、15	行业26	1.1
目标年能源消费强度降幅为基准情景的1.1倍	行业1、9、15	行业26	1.1
目标年水资源消费强度降幅为基准情景的0.9倍	行业1、8、9、12	行业26	1.3
目标年能源消费强度降幅为基准情景的0.9倍	行业1、8、9、12	行业26	1.3
目标年能源、水资源消费强度降幅均为基准情景的0.9倍	行业1、8、9、12	行业26	1.4

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