Low-carbon development pathways for resource-based cities in China under the carbon peaking and carbon neutrality goals

Abstract

Resource-based cities are important strategic bases for securing resources in China and have made great contributions to the country’s economic development. Long-term extensive resource development has made resource-based cities an important region constraining China from achieving comprehensive low-carbon development. Therefore, it is of great significance to explore the low-carbon transition path of resource-based cities for their energy greening, industrial transformation, and high-quality economic development. This study compiled the CO₂ emission inventory of resource-based cities in China from 2005 to 2017, explored the contribution to CO₂ emissions from three perspectives (driver, industry, and city), and predicted the peak of CO₂ emissions in resource-based cities. The results show that resource-based cities contribute 18.4% of the country’s GDP and emit 44.4% of the country’s CO₂ and that economic growth and CO₂ emissions have not yet been decoupled. The per capita CO₂ emissions and emission intensity of resource-based cities are 1.8 times and 2.4 times higher than the national average, respectively. Economic growth and energy intensity are the biggest drivers and main inhibitors of CO₂ emissions growth. Industrial restructuring has become the biggest inhibitor of CO₂ emissions growth. Based on the different resource endowments, industrial structures, and socio-economic development levels of resource-based cities, we propose differentiated low-carbon transition pathways. This study can provide references for cities to develop differentiated low-carbon development paths under the “double carbon” target.

Appendix

Appendix 1. GM(1,1) model construction and accuracy test

The modeling process for the GM(1,1) model is as follows.

For a sequence X⁽⁰⁾

$${X}^{(0)}(k)=\left\{\ {X}^{(0)}(1),{X}^{(0)}(2),\cdot \cdot \cdot, {X}^{(0)}(n)\ \right\}$$

its Accumulating Generation Operational Sequence (AGO Sequence) is

$${X}^{(1)}(k)=\left\{\ {X}^{(1)}(1),{X}^{(1)}(2),\cdot \cdot \cdot, {X}^{(1)}(n)\ \right\}$$

where ${X}^{(1)}(k)={\sum}_1^k{X}^{(0)}(t),\kern0.5em k=1,2,3,\cdot \cdot \cdot, \kern0.5em n$

Establish the first-order linear differential equation of X⁽¹⁾(k), i.e., GM(1,1) model:

$$\frac{dx^{(1)}}{dt}+a{x}^{(1)}=u$$

$$\frac{dx}{dt}=\underset{\Delta t\to 0}{\lim}\frac{x\left(t+\Delta t\right)-x(t)}{\Delta t}$$

$$\frac{\Delta x}{\Delta t}=x\left(k+1\right)-x(k)={a}^{(1)}\left(x\left(k+\Delta t\right)\right)$$

$$x=\frac{1}{2}\left(x\left(k+1\right)+x(k)\right)$$

Its differential equation can be written as:

$${a}^{(1)}\left({x}^{(1)}\left(k+1\right)\right)+\frac{1}{2}a\left({x}^{(1)}\left(k+1\right)+{x}^{(1)}(k)\right)=u$$

The matrix form is:

$$\left[\begin{array}{c}{x}_1^{(0)}(2)\\ {}{x}_1^{(0)}(3)\\ {}\begin{array}{c}\vdots \\ {}{x}_1^{(0)}(n)\end{array}\end{array}\right]=\left[\begin{array}{cc}-\frac{1}{2}a\left({x}_1^{(1)}(2)+{x}_1^{(1)}(1)\right)& 1\\ {}\begin{array}{c}-\frac{1}{2}a\left({x}_1^{(1)}(3)+{x}_1^{(1)}(2)\right)\\ {}\vdots \\ {}-\frac{1}{2}\left({x}_1^{(1)}(n)+{x}_1^{(1)}\left(n-1\right)\right)\end{array}& \begin{array}{c}1\\ {}\vdots \\ {}1\end{array}\end{array}\right]\left[\begin{array}{c}a\\ {}u\end{array}\right]$$

$$Y=\left[\begin{array}{c}{x}_1^{(0)}(2)\\ {}{x}_1^{(0)}(3)\\ {}\begin{array}{c}\vdots \\ {}{x}_1^{(0)}(n)\end{array}\end{array}\right],B=\left[\begin{array}{cc}-\frac{1}{2}a\left({x}_1^{(1)}(2)+{x}_1^{(1)}(1)\right)& 1\\ {}\begin{array}{c}-\frac{1}{2}a\left({x}_1^{(1)}(3)+{x}_1^{(1)}(2)\right)\\ {}\vdots \\ {}-\frac{1}{2}\left({x}_1^{(1)}(n)+{x}_1^{(1)}\left(n-1\right)\right)\end{array}& \begin{array}{c}1\\ {}\vdots \\ {}1\end{array}\end{array}\right],\hat{a}=\left[\begin{array}{c}a\\ {}u\end{array}\right]$$

$$Y=B\hat{a}$$

Solve by least squares method $\hat{a}$,

$$\hat{a}={\left({B}^TB\right)}^{-1}{B}^T$$

Substituting $\hat{a}$ into $\frac{dx^{(1)}}{dt}+a{x}^{(1)}=u$ (GM(1,1) model) and solving for it, we get:

$${\hat{X}}^{(1)}\left(k+1\right)=\left({X}^{(0)}(1)-\frac{u}{a}\right)\ {e}^{- at}+\frac{u}{a}$$

Model accuracy test:

Relative residual Q-test:

$$Q=q(k)={X}^{(0)}(k)-{\hat{X}}^0(k)$$

Variance ratio C-test:

$$C=\frac{S_2}{S_1},{S}_1^2=\frac{1}{n}{\sum}_{k=1}^n\left({X}^{(0)}(1)-\overline{X}\right),{S}_2^2=\frac{1}{n}{\sum}_{k=1}^n{\left(e(k)-\overline{e}\right)}^2$$

Where $\overline{X}=\frac{1}{n}{\sum}_{k=1}^n{X}^{(0)}(k)$，$\overline{e}=\frac{1}{n}{\sum}_{k=1}^ne(k)$

Small error probability P-test

$$P=\left(1-\upvarepsilon (avg)\right)\times 100\%,\upvarepsilon (avg)=\frac{1}{n-1}{\sum}_{k=2}^n\left|\upvarepsilon (k)\right|,\upvarepsilon (k)=\frac{q(k)}{X^{(0)}(k)}\times 100\%$$

(5-14)

Accuracy prediction level

Prediction accuracy	Excellent	Qualified	Barely qualified	Unqualified
Q	<0.01	<0.05	<0. 1	<0.2
C	<0.35	<0.5	<0.65	≥ 0.65
P	>0.95	>0.8	>0.7	≤ 0.7

Appendix 2. Supplementary table

Table 1 Eighty-four cities included in this study.

Table 2 17 types of fossil fuels included in this study.

Table 3 7 industrial processes included in this study

Table 4 47 socioeconomic sectors included in this study.

Appendix 3. Supplementary figure

See Fig. 10

Figure 10

The results of uncertainty analysis of CO₂ emissions in resource-based cities.