Understanding the importance of sustainable ecological innovation in reducing carbon emissions: investigating the green energy demand, financial development, natural resource management, industrialisation and urbanisation channels

Figures & data

Table 1. Literature review on sustainable ecological innovation and its potential factors.

Authors	Time period	Results
Wang, Su, et al. (2020)	1998–2017	RE and green investment are significant factors in limiting production-based CO2 emissions. Further, FD, human capital and environment-related technology are also helpful in limiting carbon output.
Yan et al. (2020)	1997–2015	RE technical innovation considerably influences green productivity when the provincial relative income is a vital turning point. Other than the turning point rise in relative income, green production increases.
Wang and Zhu (2020)	2001–2017	RE technology innovation helps lower carbon emissions, but fossil energy technology innovation is not environmental friendly. Hence, China’s government should push the RE innovation technologies to maintain the low carbon emission in the economy.
Ma et al. (2021)	1995–2019	Tertiary sector development and local expansion affect the carbon emission favorably. In contrast, RE consumption, energy investment, R&D spending, and technical innovation lowered carbon dioxide output in China. Results recommended that responsible entities implement emission reduction strategies and encourage RE use.
Sun et al. (2021)	1990–2017	In the long-run economic development and carbon emission are related in an inverted U-shaped connection. Economic forces and solar technology advancements counteract the carbon emission. It has been proposed that the Chinese government promote green technologies and research and development to safeguard the environment.
Liu et al. (2021)	1995–2017	There exists the long term cointegration of green investment and carbon emissions. Investment in green initiatives helps to minimise carbon output in China, and natural resource exploitation, FD and EI boost emissions.
Zahoor et al. (2022)	1970–2016	Results indicated the negative relationship between RE investment, ecological footprint and carbon emission. In comparison, RE investment and economic development are favorably associated. Although industrial value-added, financial development and URB spurred economic development but deteriorated China’s environmental quality.
Wang et al. (2022)	2000–2018	FDI has a negative influence on carbon emission. On the other hand, technology positively influences the first three quantiles, whereas it represents a negative impact in the following three quantiles. As FDI and technological innovation improve EI, contributing to the variability in carbon emissions.
Yuan et al. (2022)	2005–2017	Green investment plays a vital role in lowering carbon emissions. However, institutional quality has a negative moderating influence on the link between green investment and carbon emission. Explaining that better institutional quality leads to more green investment, resulting in reduced carbon output.
Zeng et al. (2022)	2001–2019	Despite the green technology innovation rising in China but tits efficiency rate is poor. Moreover, its overall carbon output grows while EI drops. It is evident from data that green technology investment helps significantly to minimise carbon output.

Source: Author's compilation.

Table 2. List of variables.

Variables	Symbol	Measurement	Definition	Expected sign	Theoretical support
Carbon emission	CO2	CO2 metric tons per capita	CO2 emissions are gases produced by human activities, including transportation, manufacturing, heating, and power production.
Environmental technologies	ET	Share of R&D expenditures on high-technology exports	Human activities degrade the natural environment. Cleaner technologies are used to decrease resource utilization and technology’s environmental implications.	Negative	Cleaner technologies helps to improve industrial production and achieve decarbonization agenda (Chin et al., 2020; Alataş, 2022; Khan, Ponce et al., 2022).
Technological innovation	TI	Resident and non-resident patent applications	Technological innovation is an improved product with better technical features. New ideas and technology bring new processes and items to market.	Negative	Sustainable production is possible by introducing new technologies, which helps to mitigate carbon emissions (Abid et al., 2022; Zhang et al., 2022; Zhangqi et al., 2022).
Financial development	FD	FDI inflows	Expansion of the financial system promotes growth and investment. It aims to boost growth, efficiency, and financial market access. Financial development reduces financial system costs.	Positive	Dirty production (pollution haven hypothesis) may increase in the absence of climate financing and ease of environmental regulations (Gao et al., 2022; Haq et al., 2022; Sheraz et al., 2022).
Natural resource depletion	NRD	Adjusted savings: natural resources depletion (% of GNI)	Use surpasses recovery or replacement of natural resources. This term is used in water, mining, agriculture, and fossil fuel. Resources are being depleted faster than they can be replenished.	Positive	Exploitation of natural resources damages clean and green satiability agenda, which may be limit through technological innovations (Gyamfi et al., 2022; Usman, Kousar et al., 2022; Zaman et al., 2017).
Urbanisation	URB	Urban population (% of total population)	Urbanisation is the rural-to-urban movement. Urbanisation diminishes the rural population and develops towns and cities. Cities expanded in population density as most people relocated to live and work.	Positive	The rapid urbanisation exhausts economic and environmental resources that limit through sustainable ecological policies (Musah et al., 2021; Wang et al., 2021; Wang & Wang, 2021).
Renewable energy	RE	Renewable energy consumption (% of total energy)	Renewable energy comes from natural resources, and it lowers energy costs and benefits the environment.	Negative	Switching NRE to RE sources improves environmental quality (Bouyghrissi et al., 2022; Rehman et al., 2022; Zafar et al., 2022).
Industrialisation	IND	Industrialisation value added (% of GDP)	Producing goods from raw ingredients. Machines do the job of humans, and agriculture loses out to industry due to industrialisation.	Positive	Massive urbanisation increase the risk of environmental deterioration that need resilient urban policies to limit it (Ahmed et al., 2022; Elfaki et al., 2022; Mentel et al., 2022).
GDP per capita	GDP	GDP per capita, constant 2015 US$	GDP per capita gauges economic growth. It is a financial statistic that divides gross domestic output by midyear population.	Positive	Continued economic growth supported by unsustainable production and innovation tend to worsen environmental quality (Azam et al., 2021; Cui et al., 2022; Zhong et al., 2022).

Source: Author's compilation.

Figure 1. Statistical framework of the study.

Source: Author’s construct.

Table 3. Descriptive statistics.

Methods	CO2	ET	TI	FD	REC	NRD	URB	IND	GDP
Mean	3.656	0.034	263496.7	2.336	25.345	3.542	35.829	44.478	4.20E + 12
Median	2.551	0.021	23758	2.375	29.961	2.919	33.375	45.359	2.29E + 12
Maximum	7.352	0.069	1542002	6.186	34.083	11.545	61.428	48.057	1.46E + 13
Minimum	1.250	0.018	0.126	4.49E-05	11.338	0.595	17.400	37.820	3.04E + 11
Std. Dev.	2.239	0.019	442873.2	1.821	9.395	2.787	13.895	2.610	4.40E + 12
Skewness	0.661	0.706	1.785	0.262	−0.466	1.355	0.338	−0.877	1.069
Kurtosis	1.772	1.861	4.822	1.866	1.411	4.392	1.807	2.885	2.829

Source: Author's self-calculation.

Table 4. Correlation analysis.

Variables	CO2	ET	TI	FD	REC	NRD	URB	IND	GDP
CO2	1
ET	−0.734	1
TI	0.821	−0.481	1
FD	−0.721	0.176	−0.644	1
REC	0.791	−0.966	0.539	−0.342	1
NRD	−0.489	0.761	−0.409	0.028	−0.710	1
URB	−0.887	0.957	−0.660	0.392	−0.952	0.687	1
IND	0.207	−0.430	0.268	0.367	0.241	−0.469	−0.413	1
GDP	−0.735	0.972	−0.492	0.157	−0.912	0.700	0.955	−0.549	1

Source: Author's self-calculation.

Table 5. ADF unit root estimates.

Variable	Level		First difference		Decision
	Constant	Constant and trend	Constant	Constant and trend
CO2	−1.673 (0.437)	−1.283 (0.879)	−2.968 (0.045)	−2.223 (0.093)	I(1)
ET^a	0.785^a (0.992)	−1.752^a (0.710)	−4.682^a (0.000)	−5.243^a (0.000)	I(1)
TI	−7.920 (0.000)	−7.858 (0.000)	−7.016 (0.000)	−6.000 (0.000)	I(0)
FD	−1.630 (0.459)	−1.181 (0.902)	−5.333 (0.000)	−5.419 (0.000)	I(1)
REC	−0.598 (0.860)	−2.585 (0.288)	−3.033 (0.039)	−2.986 (0.147)	I(1)
NRD	−1.724 (0.412)	−1.820 (0.678)	−5.693 (0.000)	−5.664 (0.000)	I(1)
URB	1.754 (0.999)	−1.326 (0.868)	−3.680 (0.007)	−3.776 (0.027)	I(1)
IND	−1.024 (0.736)	−1.402 (0.846)	−4.644 (0.000)	−4.805 (0.001)	I(1)

Note: ^ashows Phillips-Perron. Small bracket shows probability value.

Source: Author's self-calculation.

Table 6. VAR lag order selection criteria.

Lag	LogL	LR	FPE	AIC	SC	HQ
0	−53.52384	NA	1.48e-10	2.908086	3.276709	3.044022
1	494.8517	841.6927	5.80e-20	−18.83031	−15.14408	−17.47094
2	641.5968	163.8085	4.54e-21	−21.88822	−14.88438	−19.30542
3	803.6507	113.0609*	5.63e-22*	−25.65817*	−15.33672*	−21.85194*

Note: * shows desirable lag length.

Source: Author’s self-calculation.

Table 7. ARDL short- and long-run estimates.

Variables	Coefficient	Std. Error	t-Statistic	Prob.
D(ET)	−14.890037	2.792244	−5.332642	0.0002
D(ET(−1))	−5.900901	3.118991	−1.891926	0.0851
D(FD)	−0.039237	0.008463	−4.636196	0.0007
D(FD(−1))	0.034068	0.007691	4.429615	0.0010
D(FD(−2))	0.038886	0.010403	3.738067	0.0033
D(GDP)	1.121009	0.360267	3.111607	0.0099
D(GDP(−1))	0.426742	0.776742	0.549399	0.5937
D(GDP(−2))	1.461670	0.442035	3.306684	0.0070
D(IND)	0.006406	0.004698	1.363633	0.1999
D(IND(−1))	−0.021203	0.005663	−3.744115	0.0032
D(IND(−2))	−0.029607	0.007393	−4.004571	0.0021
D(NRD)	−0.001515	0.002625	−0.577086	0.5755
D(NRD(−1))	−0.003646	0.002715	−1.343067	0.2063
D(NRD(−2))	−0.008413	0.003531	−2.382412	0.0363
D(REC)	−0.006303	0.005240	−1.202895	0.2543
D(REC(−1))	−0.005747	0.006685	−0.859636	0.4083
D(REC(−2))	−0.005194	0.004557	−1.139834	0.2786
D(TI)	0.177911	0.078065	2.278994	0.0436
D(TI(−1))	0.330647	0.066968	4.937350	0.0004
D(TI(−2))	−0.161583	0.047424	−3.407233	0.0059
D(URB)	0.020522	0.077827	0.263689	0.7969
D(URB(−1))	−0.488451	0.120827	−4.042568	0.0019
D(URB(−2))	0.154806	0.052353	2.956971	0.0130
CointEq(−1)	−1.375036	0.274298	−5.012928	0.0004
Long Run Coefficients
Variables	Coefficient	Std. Error	t-Statistic	Prob.
ET	−8.811156	3.691962	−2.386578	0.0361
FD	−0.095750	0.010952	−8.742808	0.0000
GDP	−0.119271	0.063784	−1.869928	0.0883
IND	0.045361	0.009530	4.760036	0.0006
NRD	0.009722	0.004752	2.045863	0.0654
REC	0.005071	0.002796	1.813917	0.0970
TI	−0.016479	0.030405	−0.541987	0.5986
URB	0.010328	0.010483	0.985238	0.3457

Source: Author's self-calculation.

Table 8. Bounds test result.

F-bounds test null hypothesis: no levels relationship
ARDL bounds test					NARDL bounds test
Test statistic	Value	Significance level	I(0)	I(1)	Test statistic	Value	Significance level	I(0)	I(1)
F-statistic	7.358	10%	1.66	2.79	F-statistic	7.569	10%	3.19	2.72
K		5%	1.91	3.11	K		5%	3.83	3.22
		2.5%	2.15	3.4			2.5%	4.5	3.88
		1%	2.45	3.79			1%	5.3	3.17

Source: Author's self-calculation.

Table 9. NARDL short run results.

Variables	Coefficient	Std. Error	t-Statistic	Prob.
D(ET_POS)	−5.867078	2.220445	−2.642298	0.0138
D(ET_NEG)	−15.410759	13.010985	−1.184442	0.2470
D(FD_POS)	−0.006451	0.003918	−1.646371	0.1117
D(FD_NEG)	−0.010252	0.006889	−1.488248	0.1487
D(GDP)	0.001601	0.016651	0.096152	0.9241
D(IND)	0.003798	0.002041	1.860719	0.0741
D(NRD_POS)	0.004714	0.002493	1.891120	0.0698
D(NRD_NEG)	0.003771	0.003125	1.206653	0.2384
D(REC_POS)	−0.003924	0.007188	−0.545965	0.5897
D(REC_NEG)	0.010259	0.005060	2.027392	0.0530
D(TI_POS)	0.661962	0.597016	1.108785	0.2777
D(TI_NEG)	0.078196	0.043046	1.816562	0.0808
D(URB)	0.004518	0.007421	0.608751	0.5480
CointEq(-1)	−0.351876	0.096279	−3.654770	0.0011

Source: Author's self-calculation.

Table 10. NARDL long run results.

Variables	Coefficient	Std. Error	t-Statistic	Prob.
ET_POS	−16.673687	7.617463	−2.188877	0.0378
ET_NEG	−43.795932	39.520149	−1.108192	0.2779
FD_POS	−0.018334	0.008251	−2.222014	0.0352
FD_NEG	0.040528	0.021060	1.924406	0.0653
GDP	0.004550	0.046999	0.096811	0.9236
IND	0.010793	0.006300	1.713148	0.0986
NRD_POS	0.013397	0.009139	1.465895	0.1547
NRD_NEG	0.047370	0.014379	3.294464	0.0028
REC_POS	0.166851	0.044630	3.738566	0.0009
REC_NEG	−0.014478	0.008136	−1.779510	0.0868
TI_POS	1.881234	1.541218	1.220615	0.2332
TI_NEG	0.222225	0.120537	1.843626	0.0767
URB	0.012839	0.021025	0.610637	0.5467

Source: Author's self-calculation.

Figure 2. C.U.S.U.M. and C.U.S.U.M. of squares.

Source: Author’s illustration.

Table 11. Diagnostic results.

Test type	ARDL	NARDL
Normality test (JB) (p-value)	0.9202(0.631)	0.366(0.832)
LM test (p-value)	1.921(0.201)	0.745(0.4853)
RESET(F) (p-value)	1.318(0.277)	0.1786(0.6761)
Heteroskedasticity test(BPG) (p-value)	0.611(0.858)	1.052(0.4445)
CUSUM and CUSUM Sq & Multiplier graphs	Stable at 95% confidence interval.

Source: Author's self-calculation.

Table 12. Granger causality estimates.

Variables	CO2	ET^+	ET^−	TI^+	TI^−	FD^+	FD^−	NRD^+	NRD^−	REC^+	REC^−	URB	IND	GDP
CO2	–	↔	$\ne$	$\ne$	→	→	$\ne$	$\ne$	$\ne$	$\ne$	→	$\ne$	→	$\ne$
ET^+	↔	–	→	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	→	→	$\ne$	$\ne$	$\ne$
ET^−	→	$\ne$	–	$\ne$	→	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$
TI^+	$\ne$	$\ne$	$\ne$	–	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$
TI^−	$\ne$	$\ne$	$\ne$	$\ne$	–	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$
FD^+	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	–	→	$\ne$	→	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$
FD^−	→	$\ne$	$\ne$	→	→	$\ne$	–	→	→	→	→	$\ne$	$\ne$	$\ne$
NRD^+	→	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	–	$\ne$	$\ne$	↔	$\ne$	$\ne$	↔
NRD^−	→	$\ne$	→	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	–	$\ne$	$\ne$	$\ne$	↔	$\ne$
REC^+	→	$\ne$	$\ne$	→	→	→	$\ne$	→	$\ne$	–	→	$\ne$	$\ne$	$\ne$
REC^−	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	↔	→	$\ne$	–	$\ne$	→	$\ne$
URB	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	→	→	$\ne$	$\ne$	–	→	$\ne$
IND	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	$\ne$	↔	$\ne$	$\ne$	$\ne$	–	$\ne$
GDP	→	$\ne$	$\ne$	$\ne$	$\ne$	→	$\ne$	↔	$\ne$	$\ne$	→	$\ne$	→	–

Note: $\ne$ shows no causality, → shows unidirectional causality and ↔ shows bidirectional causality.

Source: Author's self-calculation.

Table 13. Impulse response function of CO₂.

Period	CO2	ET_NEG	ET_POS	FD_NEG	FD_POS	NRD_NEG	NRD_POS	GDP	IND	REC_NEG	REC_POS	TI_NEG	TI_POS	URB
1	0.011242	0	0	0	0	0	0	0	0	0	0	0	0	0
2	0.008594	−0.001881	0.004576	−0.000579	0.004345	0.008728	0.006272	−0.002822	0.005155	0.002873	0.003992	−0.000230	−0.000220	0.000400
3	0.005719	−0.005617	0.007003	0.006385	0.003744	0.009512	0.003621	−0.000698	0.006347	0.000739	3.63E-06	0.000940	−0.000276	−0.000266
4	0.004789	−0.005192	0.009375	0.005787	0.002191	0.009672	0.001473	0.002853	0.006946	0.002316	0.003578	0.002587	0.001165	−0.000860
5	0.000939	−0.010618	0.005169	0.004828	0.001218	0.005769	−0.003619	0.002804	0.004540	0.002020	0.002520	0.001043	0.001895	−0.000707
6	−0.001623	−0.009153	0.008317	−0.003618	0.000538	−0.000847	−0.006363	0.002371	−0.000223	−0.001102	−0.000145	−0.001113	0.000472	0.000297
7	−0.005640	−0.005115	0.006039	−0.008263	7.43E-05	−0.003734	−0.004849	0.000915	−0.002150	−0.001260	0.002004	−0.001991	−0.001122	0.001058
8	−0.008179	0.000554	0.006084	−0.013154	0.001373	−0.008747	−0.003695	−0.002973	−0.003129	−0.001698	0.002989	−0.002528	−0.002276	0.001518
9	−0.008169	0.006206	0.007172	−0.015108	0.000960	−0.007081	−0.000863	−0.005145	−0.002742	−0.001717	0.004901	−0.003055	−0.003080	0.001932
10	−0.007821	0.010408	0.008572	−0.013690	0.000531	−0.003799	0.001754	−0.005298	−0.002359	−0.001359	0.006879	−0.001440	−0.002906	0.001492

Source: Author's self-calculation.

Table 14. Variance decomposition of CO₂.

Period	S.E.	CO2	ET_NEG	ET_POS	FD_NEG	FD_POS	NRD_NG	NRD_POS	GDP	IND	REC_NEG	REC_POS	TI_NEG	TI_POS	URB
1	0.011242	100	0	0	0	0	0	0	0	0	0	0	0	0	0
2	0.020456	47.85432	0.845998	5.004574	0.080171	4.512602	18.20350	9.400925	1.903503	6.350144	1.972965	3.808733	0.012695	0.011588	0.038282
3	0.027064	31.80386	4.791449	9.553738	5.612607	4.491206	22.75191	7.160121	1.153978	9.127230	1.201690	2.175834	0.127888	0.016991	0.031496
4	0.032979	23.52708	5.705703	14.51569	6.859411	3.466215	23.92385	5.021592	1.525711	10.58280	1.302504	2.642290	0.701591	0.136333	0.089233
5	0.036650	19.11575	13.01424	13.74235	7.289497	2.917059	21.84892	5.040976	1.820642	10.10322	1.358565	2.612399	0.649071	0.377798	0.109514
6	0.039520	16.60835	16.55695	16.24797	7.107011	2.527247	18.83628	6.927611	1.925843	8.692075	1.246132	2.248041	0.637499	0.339183	0.099813
7	0.042183	16.36529	16.00266	16.31066	10.07494	2.218520	17.31641	7.402077	1.737423	7.888859	1.182958	2.198922	0.782279	0.368427	0.150565
8	0.046830	16.32889	12.99850	14.92217	16.06492	1.886018	17.53960	6.628589	1.812686	6.847435	1.091262	2.191501	0.926100	0.535085	0.227247
9	0.052089	15.65726	11.92559	13.95655	21.39680	1.558357	16.02465	5.385116	2.440760	5.811669	0.990681	2.656662	1.092508	0.782132	0.321273
10	0.057070	14.92140	13.26082	13.88256	23.57909	1.306883	13.79265	4.580595	2.895124	5.012385	0.882044	3.665876	0.973757	0.910838	0.335975

Source: Author's self-calculation.

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