An LSTM-STRIPAT model analysis of China’s 2030 CO₂ emissions peak

Figures & data

Table 1. Some literature on carbon dioxide emissions forecasting over the last decade.

Reference	Forecasting method	Results or conclusions
Fang et al. [6]	Improved PSO-GPR	China’s total carbon dioxide emissions will continue to increase but will eventually decrease.
Pao et al. [7]	Improved grey model	The MAPEs of NGBM − OP are ranging from 1.10 to 6.26
Niu et al. [8]	General regression neural network (GRNN)	Carbon emission intensity will decrease by 54.7%, 63.4%, and 60.8% in 2030 compared to 2005.
Dai et al. [9]	MSFLA-LSSVM	In the next few years, China’s carbon dioxide emissions will show a slow-growth trend.
Ding et al. [10]	Novel grey multi-variable model	The future of CO2 emissions from fuel combustion will continue to grow from 2015 to 2020.
Yuan et al. [11]	Improved grey model	CO2 emissions will peak in 2030–2035 at about 9300 Mt.
Li et al. [12]	Grey model	China will achieve the CO2 emissions peak if the GDP is lower than 151,426.15 billion Yuan by 2030.
Sun and Liu [13]	LSSVM	The LSSVM model is superior to the BPNN and GM (1,1) models.

Figure 1. (a) co-occurrence analysis of CO₂ emissions’ influencing factors. (b) co-occurrence analysis of the STIRPAT model.

Table 2. Summary of the literature on the influencing factors of carbon emissions.

Reference	Region	Method	Period	Main drivers
Behera et al. [40]	SSEA	STIRPAT	1980–2012	Urbanization, energy consumption, FDI
Haseeb et al. [28]	BRICS	STIRPAT	1990–2014	GDP per capita, urbanization, energy consumption
Fan et al. [30]	China	STIRPAT	1975–2000	GDP per capita, population, technology, urbanization, population aged 15–64
Li et al. [41]	Tianjin	STIRPAT	1996–2012	Population density, income growth, energy intensity, FDI
Yang et al. [42]	China	STIRPAT	2000–2010	GDP per capita, the share of tertiary industry, urbanization, population, energy intensity
Zhang et al. [43]	China	STIRPAT	2005–2012	GDP per capita, industrial structure, technology, energy structure, affordable urban revenue per capita, energy coefficient for urban dwellers
Wang et al. [44]	Beijing	STIRPAT	1997–2010	Urbanization level, GDP per capita, the share of the industry sector output value over the total GDP, energy intensity, R&D output
Li et al. [45]	China	STIRPAT	1990–2008	GDP per capita, Industrial structure, Urbanization rate, the economic output of per unit energy consumption, population density, energy consumption

Figure 2. Structure of LSTM-STIRPAT.

Figure 3. LSTM Structure.

Table 3. Data summary.

	Definition	Unit	Obs.	Min.	Max.	Mean	Sd.
lnCE	Total CO2 emissions	Mt	841	0.871	11.813	9.813	0.87
lnUR	The percentage of the urban population in the total population	%	841	0.408	4.386	2.693	0.93
lnGDP	GDP per capita	100 million	841	1.431	11.404	8.263	1.43
lnSEC	Share of the secondary industry output value over the total GDP	%	841	0.207	4.196	3.795	0.21
lnEC	Fossil fuel consumption	tce	841	0.922	10.569	8.681	0.92
lnEI	Energy intensity		841	−1.383	2.395	0.421	0.74
lnPD	Population density	per km^2	841	1.102	8.250	5.466	1.10

Figure 4. Pearson’s correlation plots between the variables.

Table 4. MAPE prediction results with the LSTM and the BP.

Province	MAPE			Province	MAPE
	LSTM	BPNN	GM(1,1)		LSTM	BPNN	GM(1,1)
Beijing	3.9%	4.8%	15.1 %	Hainan	4.6%	9.7%	19.9%
Tianjin	6.1%	16.4%	8.9%	Chongqing	2.1%	0.9%	6.5%
Hebei	5.2%	13.7%	11.9%	Sichuan	4.6%	22.3%	20.9%
Shanxi	2.2%	76.2%	9.3%	Guizhou	4.5%	19.5%	12.9%
Inner Mongolia	4.1%	32.3%	33.3%	Yunnan	5.5%	0.01%	23.9%
Liaoning	3.6%	75.0%	7.7%	Shaanxi	6.6%	58.6%	17.7%
Jilin	3.4%	72.9%	11.7%	Gansu	5.4%	0.07%	7.6%
Heilongjiang	2.6%	85.5%	10.5%	Qinghai	5.5%	4.2%	11.8%
Shanghai	3.6%	76.2%	8.8%	Ningxia	5.4%	0.36%	26.6%
Jiangsu	3.3%	32.3%	11.9%	Xinjiang	6.2%	72.2%	25.1%
Zhejiang	3.4%	85.6%	21.5%	Henan	3.2%	38.4%	21.4%
Anhui	3.8%	75.8%	7.1%	Hubei	4.4%	17.7%	11.3%
Fujian	4.7%	9.8%	24.0%	Hunan	5.5%	16.8%	20.2%
Jiangxi	5.0%	5.4%	11.7%	Guangdong	4.0%	90.2%	14.6%
Shandong	3.4%	21.1%	21.6%	Guangxi	6.0%	0.21%	14.7%

Table 5. Unit root test for stationarity.

Explanatory Variables	ADF test	PP test	Stationary
lnEC	−6.1601**	−99.977**	Yes
lnUR	−9.4011**	−7.9821**	Yes
lnGDP	−8.0084**	−142.36**	Yes
lnSEC	−3.4627**	−79.242**	Yes
lnEC	−5.6252**	−97.009**	Yes
lnEI	−7.1998**	−153.16**	Yes
lnPD	−2.4587*	−17.355*	Yes

*p < 0.1; **p < 0.05.

Table 6. Estimated results for the PWP and PWTP.

Explanatory Variables	OLS Model		Fixed Effect Model		Random Effect Model
	PWP	PWTP	PWP	PWTP	PWP	PWTP
Interept	−4.853** (1.384)	−5.035*** (0.515)			1.817 (2.501)	−1.128* (0.674)
lnUR	0.120** (0.018)	−0.170 (0.130)	−0.940 (0.054)	−0.300*** (0.074)	−0.671 (0.418)	−0.240*** (0.068)
lnGDP	−0.006*** (0.001)	0.379*** (0.079)	−0.004*** (0.0004)	0.247*** (0.045)	−0.004*** (0.0004)	0.258*** (0.046)
lnSEC	1.353*** (0.021)	−2.595*** (0.193)	1.410** (0.001)	0.107* (0.125)	1.512** (0.003)	0.108 (0.124)
lnEC	0.036*** (0.001)	0.659*** (0.098)	0.032*** (0.001)	0.631*** (0.087)	0.032*** (0.001)	0.532*** (0.073)
lnEI	2.064*** (1.244)	4.288 (3.940)	2.752*** (1.606)	1.755 (3.803)	1.935*** (1.598)	4.288 (3.940)
lnPD	0.024*** (0.007)	0.166*** (0.034)	0.058*** (0.015)	0.744* (0.409)	0.047*** (0.013)	0.086 (0.138)
Obs.	616	224	616	224	616	224
R-Squared	0.841	0.804	0.898	0.922	0.895	0.916
Adj. R-Squared	0.840	0.799	0.893	0.918	0.894	0.914
F-statistic	538.293*** (df = 6; 609)	178.341*** (df = 5; 218)	860.925*** (df = 6; 588)	500.971*** (df = 5; 211)	5,177.392***	2,388.918***
F test	F 1 = 65.365, df1 = 21, df2 = 588, p-value < 2.2e-16		F2 = 205.2, df1 = 7, df2 = 211, p-value < 2.2e-16
Hausman Test			chisq = 3.4122, df = 6, p-value = 0.7556		chisq = 26.843, df = 5, p-value = 6.12e-05

* p < 0.1;

** p < 0.05;

*** p < 0.01.

Figure 6. Rank for the CO₂ emissions drivers.

Table 7. Empirical results for the provinces without a CO₂ emission peak value (dynamic).

Explanatory Variables	OLS Model	Fixed Effect Model	Random Effect Model
lag(lnCE, 1)	0.968*** (0.010 )	0.852*** (0.017)	0.968*** (0.010)
lnUR	3.118 (6.403)	−2.6481* (1.4835)	3.118 (6.403)
lnGDP	−0.001*** (0.0002)	0.002*** (0.0002)	−0.001*** (0.0002)
lnSEC	5.6819*** (1.933)	4.9112*** (1.6283)	5.6819*** (1.293)
lnEC	0.003*** (0.0005)	0.007*** (0.001)	0.003*** (0.0005)
lnEI	0.385 (0.675)	0.343 (0.813)	0.385 (0.675)
lnPD	−0.001 (0.002)	0.0002 (0.006)	−0.001 (0.002)
Constant	−25.536*** (6.474)		−25.536*** (6.474)
Obs.	594	594	594
R-Squared	0.990	0.957	0.990
Adj. R-Squared	0.990	0.952	0.990
F-statistic	8,018.272*** (df = 7; 586)	1,694.099*** (df = 7; 539)	56,127.900***
F test	F = 8.6026, df1 = 47, df2 = 539, p-value < 2.2e-16
Hausman test		chisq = 89.362, df = 7, p-value < 2.2e-16

* p < 0.1;

** p < 0.05;

*** p < 0.01.

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