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New quality productivity and industrial structure in china: the moderating effect of environmental regulation.

1. Introduction

2. literature review, 2.1. literature on new quality productivity and industrial structure, 2.2. literature on environmental regulation and industrial structure, 3. theoretical analysis and hypothesis development, 3.1. the connotation of new quality productivity, 3.2. analysis of the mechanism between new quality productivity and industrial structure, 3.3. analysis of the mechanism of new quality productivity, environmental regulation and industrial structure, 4. methods and data, 4.1. construction and measurement of new quality productivity index system, 4.1.1. construction of new quality productivity index system, 4.1.2. measurement of new quality productivity index system, 4.2. variable selection specification, 4.3. econometric modeling, 5. results and discussion, 5.1. analysis of the results of the neoplasm productivity measurements, 5.1.1. overall analysis of new quality productivity measurement results, 5.1.2. analysis of new quality productivity measurement results in different dimensions, 5.2. benchmark regression and robustness test results, 5.3. endogeneity issues and robustness tests, 5.4. moderating effects test of environmental regulation, 6. conclusions and prospect, 6.1. conclusions, 6.2. prospect, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Click here to enlarge figure

Indicator Dimension	Primary Indicator	Secondary Indicator	Measurement Method	Direction of Effect
Innovation driving force	Innovation input	Scientific research fund	Internal expenditure on R&D/GDP	+
		Scientific manpower	R&D full-time personnel	+
		The optimization of the labor market	Number of students in colleges and universities/total employment population	+
	Innovation output	Patent output	Number of domestic patent applications granted	+
		High-tech industry output	High-tech industry new product sales revenue/GDP	+
		Labor productivity	GDP/total employment population	+
Green driving force	Resource consumption	Energy intensity	Energy consumption/GDP	−
		Land resources	Population density	−
		Atmospheric resources	SO emissions	−
	Green and environmental protection	Greening rate	Forest coverage rate	+
		Urban environmental protection	Investment in the urban environment	+
		Greenhouse effect	CO emissions	−
	Terminal pollution control	Domestic garbage disposal capacity	Domestic garbage harmless treatment rate	+
		Solid waste treatment capacity	Common industrial solid wastes utilized/common industrial solid wastes generated	+
		Wastewater Treatment capacity	Daily treatment capacity of Wastewater	+
productivity driving force	Traditional infrastructure	Transportation resources	(Highway Miles + Railroad Miles)/Jurisdictional Area	+
		Educational resources	Number of colleges and universities per 10,000 people	+
		Medical resources	Number of beds in medical and health institutions	+
	Digital economy development	Internet-related output	Total telecommunications business per capita	+
		Digital economy employment level	Number of employees in the information transmission, software, and information technology services industry/employed population in urban organizations	+
		Internet Penetration Rate	The number of Internet users per 100 people	+
		Digital Inclusive Finance	Digital Inclusive Finance index	+

(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Types	Variables	Indicators	Obs	Mean	SD	Min	Max
Explained variables	rat	Rationalization of industries	330	0.1524	0.0937	0.0082	0.4515
Explained variables	upg	Ppgrading of industries	330	1.3415	0.7320	0.5271	5.2440
Core explanatory variables	nqp	New quality productivity	330	0.3883	0.0775	0.2288	0.6418
	ino	Innovation driving force	330	0.2406	0.1254	0.0275	0.6757
	gre	Green Driving Force	330	0.4776	0.0814	0.2977	0.7161
	pro	Production Driving Force	330	0.3359	0.0993	0.1133	0.6121
Moderating variable	evi	Environmental Regulation	330	11.3379	12.0869	0.0860	110.3389
Control variables	cos	Consumption level	330	0.3801	0.0683	0.2220	0.5384
	fdi	Foreign investment	330	0.8381	0.8092	0.0003	3.5760
	tra	Foreign trade	330	0.2653	0.2908	0.0076	1.5482
	gov	Government intervention	330	0.2487	0.1025	0.1066	0.6430
	tax	Tax burden level	330	0.0819	0.0293	0.0443	0.1997

Province	District	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Beijing	1	0.4444	0.4734	0.4946	0.5188	0.5202	0.5322	0.5375	0.5503	0.5648	0.5556	0.5691
Tianjin	1	0.3986	0.4093	0.4180	0.4232	0.4308	0.4326	0.4336	0.4416	0.4498	0.4801	0.4988
Hebei	1	0.2549	0.2585	0.2683	0.2781	0.2951	0.3209	0.3427	0.3515	0.3732	0.4070	0.4226
Shanxi	2	0.2288	0.2566	0.2579	0.2629	0.2650	0.2830	0.3032	0.3210	0.3352	0.3520	0.3640
Inner Mongolia	3	0.2671	0.2722	0.2926	0.2989	0.3021	0.3140	0.3257	0.3272	0.3360	0.3507	0.3671
Liaoning	4	0.3145	0.3329	0.3422	0.3408	0.3422	0.3694	0.3813	0.3967	0.4033	0.4235	0.4448
Jilin	4	0.3079	0.3199	0.3416	0.3452	0.3477	0.3651	0.3735	0.3930	0.4089	0.4234	0.4378
Heilongjiang	4	0.2977	0.3159	0.3334	0.3347	0.3369	0.3499	0.3476	0.3648	0.3770	0.3924	0.4047
Shanghai	1	0.3857	0.3986	0.4140	0.4167	0.4249	0.4248	0.4400	0.4640	0.4789	0.4984	0.5115
Jiangsu	1	0.4024	0.4282	0.4501	0.4583	0.4657	0.4690	0.4806	0.5137	0.5268	0.5684	0.5825
Zhejiang	1	0.4079	0.4358	0.4549	0.4640	0.4798	0.4867	0.4986	0.5310	0.5498	0.5809	0.5933
Anhui	2	0.3357	0.3524	0.3697	0.3755	0.3898	0.3938	0.4130	0.4275	0.4405	0.4755	0.4960
Fujian	1	0.3695	0.3960	0.4092	0.4125	0.4161	0.4201	0.4298	0.4524	0.4600	0.4802	0.4991
Jiangxi	2	0.3225	0.3372	0.3421	0.3508	0.3599	0.3629	0.3826	0.4101	0.4397	0.4517	0.4748
Shandong	1	0.3516	0.3715	0.3969	0.4028	0.4101	0.4257	0.4480	0.4649	0.4671	0.4991	0.5218
Henan	2	0.2672	0.2878	0.3101	0.3201	0.3357	0.3564	0.3965	0.4151	0.4343	0.4706	0.4960
Hubei	2	0.3468	0.3598	0.3745	0.3890	0.3938	0.4189	0.4255	0.4471	0.4679	0.4819	0.5010
Hunan	2	0.3271	0.3415	0.3545	0.3651	0.3785	0.3874	0.4052	0.4293	0.4455	0.4715	0.4839
Guangdong	1	0.4061	0.4235	0.4427	0.4566	0.4736	0.4833	0.5078	0.5572	0.5811	0.6211	0.6418
Guangxi	3	0.3288	0.3427	0.3559	0.3600	0.3678	0.3731	0.3828	0.3970	0.4104	0.4350	0.4380
Hainan	1	0.3342	0.3537	0.3647	0.3641	0.3744	0.3779	0.3794	0.3930	0.4093	0.4227	0.4352
Chongqing	3	0.3407	0.3529	0.3730	0.3826	0.3974	0.4049	0.4173	0.4380	0.4534	0.4785	0.4937
Sichuan	3	0.3007	0.3180	0.3357	0.3493	0.3768	0.3868	0.4010	0.4339	0.4480	0.4732	0.4956
Guizhou	3	0.2420	0.2617	0.2746	0.3087	0.3243	0.3419	0.3545	0.3802	0.4015	0.4279	0.4437
Yunnan	3	0.2879	0.2962	0.3254	0.3338	0.3460	0.3524	0.3673	0.3862	0.4057	0.4233	0.4386
Shaanxi	3	0.2992	0.3165	0.3341	0.3460	0.3648	0.3852	0.3814	0.3945	0.4016	0.4305	0.4244
Gansu	3	0.2516	0.2611	0.2774	0.2828	0.2903	0.3074	0.3171	0.3379	0.3667	0.3719	0.3814
Qinghai	3	0.2742	0.2779	0.2810	0.2927	0.2933	0.3047	0.3165	0.3338	0.3461	0.3524	0.3677
Ningxia	3	0.2873	0.2996	0.3114	0.3257	0.3194	0.3209	0.3294	0.3457	0.3371	0.3555	0.3713
Xinjiang	3	0.2372	0.2455	0.2507	0.2524	0.2779	0.2867	0.3037	0.3165	0.3178	0.3406	0.3494

Province	New Quality Productivity Index	Ranking	Innovation Driving Force Index	Ranking	Green Driving Force Index	Ranking	Production Driving Force Score	Ranking
Beijign	0.5237	1	0.3726	5	0.6062	2	0.5203	1
Tianjin	0.4379	6	0.4242	4	0.4840	15	0.3752	8
Hebei	0.3248	25	0.1864	19	0.3838	27	0.3281	15
Shanxi	0.2936	29	0.1853	20	0.3384	30	0.3029	20
Inner Mongolia	0.3140	26	0.1642	23	0.3650	28	0.3307	14
Liaoning	0.3719	17	0.2491	12	0.4429	20	0.3544	12
Jilin	0.3694	20	0.1994	17	0.4792	16	0.2935	24
Heilongjiang	0.3505	22	0.1950	18	0.4332	22	0.3213	17
Shanghai	0.4416	5	0.3687	6	0.5082	11	0.3847	5
Jiangsu	0.4860	4	0.4965	1	0.5473	5	0.3767	7
Zhejiang	0.4984	3	0.4508	3	0.6169	1	0.3580	11
Anhui	0.4063	11	0.2400	13	0.5314	8	0.3189	18
Fujian	0.4314	8	0.2777	8	0.5570	4	0.3148	19
Jiangxi	0.3849	14	0.2247	14	0.4951	13	0.2943	23
Shandong	0.4327	7	0.3463	7	0.4935	14	0.4016	4
Henan	0.3718	18	0.2191	15	0.4332	23	0.3815	6
Hubei	0.4188	9	0.2530	10	0.5261	9	0.3750	9
Hunan	0.3990	12	0.2141	16	0.5063	12	0.3595	10
Guangdong	0.5086	2	0.4767	2	0.6012	3	0.4063	3
Guangxi	0.3810	16	0.1316	26	0.5352	6	0.2580	28
Hainan	0.3826	15	0.1379	25	0.5247	10	0.2497	29
Chongqing	0.4120	10	0.2634	9	0.5336	7	0.2977	21
Sichuan	0.3926	13	0.1765	21	0.4679	18	0.4177	2
Guizhou	0.3419	23	0.1274	27	0.4496	19	0.2921	25
Yunnan	0.3603	21	0.0877	29	0.4727	17	0.3270	16
Shaanxi	0.3707	19	0.2519	11	0.4384	21	0.3474	13
Gansu	0.3132	27	0.1484	24	0.3980	26	0.2623	27
Qinghai	0.3128	28	0.0816	30	0.4046	24	0.2485	30
Ningxia	0.3276	24	0.1696	22	0.4012	25	0.2814	26
Xinjiang	0.2889	30	0.1010	28	0.3523	29	0.2961	22

	(1)	(2)	(3)	(4)
	Rat	Ino	Gre	Pro
nqp	−0.6228 ***
	(0.0884)
ino		−0.4507 ***
		(0.0706)
gre			−0.7016 ***
			(0.0929)
pro				−0.4172 ***
				(0.0518)
cos	−0.2001 **	−0.2640 **	−0.1745 **	−0.1560 *
	(0.0868)	(0.0979)	(0.0803)	(0.0936)
fdi	−0.0031	−0.0012	−0.0096	−0.0073
	(0.0079)	(0.0085)	(0.0077)	(0.0071)
tra	−0.0830 ***	−0.0724 ***	−0.0888 ***	−0.0882 ***
	(0.0272)	(0.0241)	(0.0268)	(0.0331)
gov	−0.3002	−0.4197 *	−0.2931 *	−0.1048
	(0.2139)	(0.2130)	(0.1730)	(0.1527)
tax	0.7976 **	1.0145 **	0.9029 **	0.2676
	(0.3363)	(0.3760)	(0.3740)	(0.2652)
evi	0.0003	0.0004	0.0008**	0.0002
	(0.0003)	(0.0004)	(0.0003)	(0.0003)
_cons	0.5009 ***	0.3984 ***	0.5756 ***	0.3836 ***
	(0.0530)	(0.0452)	(0.0614)	(0.0389)
Hausman	14.35	14.70	−148.48	6.23
p-Value	0.0259	0.0401	-	0.5126
R	0.650	0.580	0.586	0.665
N	330	330	330	330

	(1)	(2)	(3)	(4)
	Upg	Ino	Gre	Pro
nqp	2.5179 ***
	(0.4119)
ino		1.4677 ***
		(0.4265)
gre			2.5862 ***
			(0.4729)
pro				1.7481 ***
				(0.2264)
cos	0.4155	0.7269 **	0.4388	0.2349
	(0.3237)	(0.3295)	(0.3400)	(0.3142)
fdi	−0.0066	−0.0027	0.0080	−0.0029
	(0.0493)	(0.0674)	(0.0546)	(0.0464)
tra	−0.8545 ***	−0.9306 ***	−0.8993 ***	−0.8746 ***
	(0.2848)	(0.3059)	(0.2951)	(0.2339)
gov	3.7884 ***	4.4497 ***	4.3143 ***	3.1945 ***
	(0.8260)	(0.786)	(0.8920)	(0.7256)
tax	−5.8453 **	−7.5855 ***	−7.8955 ***	−4.0714 **
	(2.1639)	(2.3196)	(2.1473)	(1.8121)
evi	0.0007	−0.0001	−0.0008	0.0018 **
	(0.0009)	(0.0014)	(0.0011)	(0.0008)
_cons	−0.0336	0.4769 ***	−0.2454	0.4177 ***
	(0.1572)	(0.1430)	(0.2065)	(0.1196)
Hausman	294.44	178.05	459.34	3428.56
p-Value	0.0000	0.0000	0.0000	0.0000
R	0.740	0.669	0.705	0.776
N	330	330	330	330

	(1)	(2)	(3)	(4)
	Sys-GMM		Diff-GMM
	Rat	Upg	Rat	Upg
L.rat	0.7200 ***		0.7552 ***
	(0.0204)		(0.0187)
L.upg		0.9922 ***		0.6629 ***
		(0.0399)		(0.0390)
nqp	−0.1121 ***	0.7525 ***	−0.0439 ***	0.3572 **
	(0.0187)	(0.1096)	(0.0161)	(0.1772)
cos	−0.0121	0.4463 ***	−0.0130 *	0.2589 ***
	(0.0087)	(0.0779)	(0.0067)	(0.0392)
fdi	−0.0120 ***	−0.0889 ***	−0.0051 ***	−0.0206 **
	(0.0036)	(0.0212)	(0.0014)	(0.0084)
tra	−0.0740 ***	−0.1777 ***	−0.0001	−0.6128 ***
	(0.0194)	(0.0393)	(0.0077)	(0.1028)
gov	−0.3254 ***	1.3933 ***	−0.3838 ***	2.2454 ***
	(0.0288)	(0.3295)	(0.0234)	(0.1483)
tax	0.3911 ***	−0.3031	0.7349 ***	−2.6063 ***
	(0.1013)	(0.5166)	(0.0933)	(0.4328)
evi	−0.0001 ***	−0.0001	−0.0001 ***	0.0005
	(0.0000)	(0.0004)	(0.0000)	(0.0003)
_cons	0.1501 ***	−0.2537 ***	0.0862 ***	0.0341
	(0.0102)	(0.0974)	(0.0100)	(0.0636)
AR(1)-P	0.0135	0.0431	0.0176	0.1144
AR(2)-P	0.0564	0.1158	0.1069	0.2759
Sargan	28.0614	26.0649	26.9213	23.6456
p-Value	0.7911	0.9807	0.3598	0.8570
N	263	263	223	223

	(1)	(2)	(3)	(4)	(5)	(6)
	Benchmark Regression		Sys-GMM		Diff-GMM
	Rat	Upg	Rat	Upg	Rat	Upg
L.rat			0.7125 ***		0.7287 ***
			(0.0205)		(0.0211)
L.upg				0.9823 ***		0.6250 ***
				(0.0379)		(0.0360)
nqp	−0.5742 ***	2.6335 ***	−0.1051 ***	0.7579 ***	−0.0550 ***	0.4162 **
	(0.0767)	(0.4859)	(0.0300)	(0.1588)	(0.0186)	(0.1982)
evi	0.0065 ***	0.0090	0.0015 ***	−0.0131 ***	0.0013 ***	−0.0049 **
	(0.0020)	(0.0095)	(0.0003)	(0.0026)	(0.0003)	(0.0025)
evnqp	−0.0194 ***	−0.0260	−0.0051 ***	0.0380 ***	−0.0045 ***	0.0167 **
	(0.0062)	(0.0301)	(0.0009)	(0.0084)	(0.0009)	(0.0074)
cos	−0.1557 **	0.4530	0.0187 **	0.3827 ***	−0.0108	0.3762 ***
	(0.0776)	(0.3144)	(0.0087)	(0.0917)	(0.0097)	(0.0643)
fdi	−0.0026	−0.0047	−0.0111**	−0.0546**	−0.0030	−0.0231
	(0.0083)	(0.0468)	(0.0053)	(0.0265)	(0.0018)	(0.0150)
tra	−0.0943 ***	−0.8557 ***	−0.0806 ***	−0.1629 ***	−0.0001	−0.5476 ***
	(0.0276)	(0.2899)	(0.0203)	(0.0560)	(0.0084)	(0.1183)
gov	−0.1855	3.8440 ***	−0.3345 ***	1.4725 ***	−0.3579 ***	2.3434 ***
	(0.1589)	(0.8234)	(0.0402)	(0.3534)	(0.0327)	(0.1488)
tax	0.7018 **	−5.6357 **	0.5115 ***	0.1629	0.7793 ***	−3.3264 ***
	(0.2858)	(2.2077)	(0.0860)	(0.7969)	(0.0980)	(0.4858)
_cons	0.4536 ***	−0.11657	0.1423 ***	−0.2424 *	0.0849 ***	0.0267
	(0.0444)	(0.1971)	(0.0167)	(0.1279)	(0.0155)	(0.1119)
Hausman	12.34	316.71
p-Value	0.0900	0.0000
R	0.683	0.742
AR(1)-P			0.0115	0.0461	0.0157	0.1737
AR(2)-P			0.0760	0.1498	0.1446	0.2314
Sargan			28.0984	23.4695	27.5126	22.5209
p-Value			0.9615	0.9933	0.3308	0.6055
N	330	330	263	263	223	223

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Shao, C.; Dong, H.; Gao, Y. New Quality Productivity and Industrial Structure in China: The Moderating Effect of Environmental Regulation. Sustainability 2024 , 16 , 6796. https://doi.org/10.3390/su16166796

Shao C, Dong H, Gao Y. New Quality Productivity and Industrial Structure in China: The Moderating Effect of Environmental Regulation. Sustainability . 2024; 16(16):6796. https://doi.org/10.3390/su16166796

Shao, Changhua, Han Dong, and Yuan Gao. 2024. "New Quality Productivity and Industrial Structure in China: The Moderating Effect of Environmental Regulation" Sustainability 16, no. 16: 6796. https://doi.org/10.3390/su16166796

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How to cite ChatGPT

Use discount code STYLEBLOG15 for 15% off APA Style print products with free shipping in the United States.

We, the APA Style team, are not robots. We can all pass a CAPTCHA test , and we know our roles in a Turing test . And, like so many nonrobot human beings this year, we’ve spent a fair amount of time reading, learning, and thinking about issues related to large language models, artificial intelligence (AI), AI-generated text, and specifically ChatGPT . We’ve also been gathering opinions and feedback about the use and citation of ChatGPT. Thank you to everyone who has contributed and shared ideas, opinions, research, and feedback.

In this post, I discuss situations where students and researchers use ChatGPT to create text and to facilitate their research, not to write the full text of their paper or manuscript. We know instructors have differing opinions about how or even whether students should use ChatGPT, and we’ll be continuing to collect feedback about instructor and student questions. As always, defer to instructor guidelines when writing student papers. For more about guidelines and policies about student and author use of ChatGPT, see the last section of this post.

Quoting or reproducing the text created by ChatGPT in your paper

If you’ve used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. For literature reviews or other types of essays or response or reaction papers, you might describe how you used the tool in your introduction. In your text, provide the prompt you used and then any portion of the relevant text that was generated in response.

Unfortunately, the results of a ChatGPT “chat” are not retrievable by other readers, and although nonretrievable data or quotations in APA Style papers are usually cited as personal communications , with ChatGPT-generated text there is no person communicating. Quoting ChatGPT’s text from a chat session is therefore more like sharing an algorithm’s output; thus, credit the author of the algorithm with a reference list entry and the corresponding in-text citation.

When prompted with “Is the left brain right brain divide real or a metaphor?” the ChatGPT-generated text indicated that although the two brain hemispheres are somewhat specialized, “the notation that people can be characterized as ‘left-brained’ or ‘right-brained’ is considered to be an oversimplification and a popular myth” (OpenAI, 2023).

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

You may also put the full text of long responses from ChatGPT in an appendix of your paper or in online supplemental materials, so readers have access to the exact text that was generated. It is particularly important to document the exact text created because ChatGPT will generate a unique response in each chat session, even if given the same prompt. If you create appendices or supplemental materials, remember that each should be called out at least once in the body of your APA Style paper.

When given a follow-up prompt of “What is a more accurate representation?” the ChatGPT-generated text indicated that “different brain regions work together to support various cognitive processes” and “the functional specialization of different regions can change in response to experience and environmental factors” (OpenAI, 2023; see Appendix A for the full transcript).

Creating a reference to ChatGPT or other AI models and software

The in-text citations and references above are adapted from the reference template for software in Section 10.10 of the Publication Manual (American Psychological Association, 2020, Chapter 10). Although here we focus on ChatGPT, because these guidelines are based on the software template, they can be adapted to note the use of other large language models (e.g., Bard), algorithms, and similar software.

The reference and in-text citations for ChatGPT are formatted as follows:

Parenthetical citation: (OpenAI, 2023)
Narrative citation: OpenAI (2023)

Let’s break that reference down and look at the four elements (author, date, title, and source):

Author: The author of the model is OpenAI.

Date: The date is the year of the version you used. Following the template in Section 10.10, you need to include only the year, not the exact date. The version number provides the specific date information a reader might need.

Title: The name of the model is “ChatGPT,” so that serves as the title and is italicized in your reference, as shown in the template. Although OpenAI labels unique iterations (i.e., ChatGPT-3, ChatGPT-4), they are using “ChatGPT” as the general name of the model, with updates identified with version numbers.

The version number is included after the title in parentheses. The format for the version number in ChatGPT references includes the date because that is how OpenAI is labeling the versions. Different large language models or software might use different version numbering; use the version number in the format the author or publisher provides, which may be a numbering system (e.g., Version 2.0) or other methods.

Bracketed text is used in references for additional descriptions when they are needed to help a reader understand what’s being cited. References for a number of common sources, such as journal articles and books, do not include bracketed descriptions, but things outside of the typical peer-reviewed system often do. In the case of a reference for ChatGPT, provide the descriptor “Large language model” in square brackets. OpenAI describes ChatGPT-4 as a “large multimodal model,” so that description may be provided instead if you are using ChatGPT-4. Later versions and software or models from other companies may need different descriptions, based on how the publishers describe the model. The goal of the bracketed text is to briefly describe the kind of model to your reader.

Source: When the publisher name and the author name are the same, do not repeat the publisher name in the source element of the reference, and move directly to the URL. This is the case for ChatGPT. The URL for ChatGPT is https://chat.openai.com/chat . For other models or products for which you may create a reference, use the URL that links as directly as possible to the source (i.e., the page where you can access the model, not the publisher’s homepage).

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