Preamble

Scientific Technological Innovation, Industrial Structure Economic Growth:

Empirical Evidence China Sanglin ,Deng

1 School

Engineering Management, Hunan University Finance Economics, Changsha, 410205, China 2School Business,Macau University Science Technology,Macau,China,999078 author:Sanglin

Abstract

study focuses synergistic relationship between technological innovation (TI), industrial structure (IS), economic growth (EG), aiming clarify their mechanism, heterogeneity, policy-driven effects.

Using Chinese listed companies (1990 2023) empirical

methods

(mediation effect analysis, fixed effects model, group regression, threshold model, difference-in-differences), explores their synergies. findings: significantly promotes upgrading, which positively drives synergistic effects; Capacity utilization partially mediates TI-IS-EG optimizing resource allocation; Heterogeneity evident: central regions lowest transformation benefits, while western regions benefit policies; services high-tech manufacturing drive traditional industries; Threshold effects exist: thresholds (strongest growth industry-service synergy), single threshold supply-side

structural reform has significant marginal incentives.

provides empirical evidence China micro-level innovation-structure-growth policy insights differentiated regional/industrial policies.

Keywords

:Technological innovation, Industrial structure, Economic growth, Capacity utilization, Supply structural reform,Threshold regression,Listed company

1 Introduction

Since 2012, China's economy experienced downward growth severe overcapacity 2025) first years China's reform opening-up, average annual growth reached 9.9%. government always taken "Eight Guarantees" bottom actual growth target.

Since 2012, growth dropped 7.7%, further dropped After years high-speed economic growth, China's growth dropped downward pressure economy entered normal" medium high-speed development Zhang, Chen, Holbig, al,2025 Against backdrop China economic downturn severe overcapacity since 2012, contradiction manifests distortion industrial structure structural imbalance characterized excess capacity heavy industries (steel, coal, cement, etc.) which directly leads continuous negative growth producer price index (PPI) (March September 2016) operational losses industrial enterprises Zhou, al,2025) pointed major overcapacity industries declined consecutive months, contributing industrial decline; since 2003, these industries suffered losses, profits dropping Song, al,2025) indicates imbalance constraint economic growth (EG), irrational resource allocation industries wastes production factors suppresses overall efficiency economic system. failure demand-side "troika" drive sustained further highlights necessity addressing supply-side structural issues. (2016) argued China economic slowdown stems decline supply-side investment returns slowdown total factor productivity (TFP) growth which inherently linked technological innovation adjustment. perspective Pareto optimality economic structuralism, improving quality quantity relies optimizing inter-industry resource allocation realizing factor optimal allocation through transformation Gyau, al,2025; owever, transformation cannot achieved without driving force growth, supply-side efficiency improvement, essentially depends (e.g., technological breakthroughs, process optimization), Taken People's Daily Online's "Economic Growth:

Transitioning Speed Growth Medium Speed Growth", February 2017,

which provides fundamental impetus shift toward value-added high-efficiency sectors. forms initial logical chain: drives transformation, optimization promotes solve problem imbalanced institutional construction, "supply structural reform" proposed, "three links, reduction, supplement" measures.

Among them, reduction overcapacity above-mentioned industries solve problem historical overcapacity, increase industrial alleviate distortion. specific institutional tools mainly serve adjust infrastructure services, attempting eliminate excess capacity create space upgrading infrastructure services.

However, reform achieved short-term fluctuations, rather long-term transformation: although briefly recovered 2016, declined again 2018, economic growth still showed downward trend, slight rebound beginning fundamental reason support adjustment driven there technological breakthrough upgrade traditional industries cultivate high-tech industries, optimization still superficial formed sustainable driving force economic growth.

China's subsequent proposal quality productivity" (September release "Guiding Catalogue Industrial Structure Adjustment (2024 Edition)" (early 2024) clarified direction industrial structure transformation identify industries encouraged, restricted, eliminated.

China further emphasizes "innovation driven development strategy" fundamental measure promote supply enhance internal vitality economic growth. again confirms position technological innovation transformation information systems: current deficiencies China's technological innovation system, long-term independent innovation capabilities enterprises corporate culture prioritizes output technology, directly hinder improvement technological innovation capabilities block transmission technological innovation structural transformation.

Since 2024, China introduced series supportive policies address issue, including "Implementation Opinions Promoting Future Industrial Innovation Development" (seven ministries), "Guidelines Innovation Point System (National Trial)" (Ministry Science Technology), "Implementation Patent Industrialization Promote

Growth Small Medium sized Enterprises" (including departments including CNIPA).

These policies enhance technological innovation capabilities term, rather causing short-term industrial fluctuations previous measures industrial structural transformation.

Based logical context, three questions regarding relationships between variables addressed: intrinsic transmission mechanism among transformation, Specifically, effectively drive transformation, further realize long-term through transformation?

After clarifying causal chain transformation industrial policies (e.g., TI-support policies, adjustment policies) regulate chain strengthen synergistic effect three variables? existing TI-support guidance policies effectively remove obstacles transformation path, thereby achieving expected long-term promotion effect? answer these questions, study draws macroeconomics econometrics theories, integrating qualitative quantitative

methods

clarify logical relationships among transformation, through theoretical deduction empirical analysis. further explores industrial policies intervene interaction these variables, proposes targeted policy recommendations transformation. research provide evidence-based, quantitative, scientific decision support government departments, enterprises, research institutions formulating policies related adjustment, promotion. remaining content study arranged follows:

Literature review Empirical

conclusions

prospects Literature review dynamic relationship between industrial structure transformation, economic growth, technological innovation always issue economics, management, political economy. context profound adjustment global economic pattern, round scientific technological revolution accelerated evolution industrial transformation, understanding internal relationship between three great significance promoting high-quality development realizing Chinese modernization. article reviews

latest research

results

industrial structure upgrading, economic growth drivers, technological innovation mechanisms, analyzes dimensions theoretical basis, empirical analysis, regional differences policy implications, aiming provide comprehensive research perspective policy reference

academic community and practitioners.

exploring promotion influence industrial structure, variable inevitably appear namely, technological innovation technological progress. summarizing previous studies, relationship between scientific technological innovation industrial structure summarized following perspectives: technological progress intermediary industrial structure transformation, which provides basic conditions facilities technological progress drives development technological level industrial environment, while changes technological level directly affect economic growth al,2025; Acemoglu 2008; Alvarez-Cuadrado .2018) scholars believe direct contribution upgrading industrial structure economic growth promotion total factor productivity (TFP) Francisco,Alvarez-Cuadrado,Ngo,et ,2017; Aghion Howitt ,1988, Aghion Howitt ,1992, al,2024 promotion industrial structure upgrading productivity realized mainly through mechanisms: intra-sectoral inter-sectoral effects. intra-sector effect refers industry achieving efficiency improvement through technological innovation management optimization, intersectoral effect reflected improvement resource allocation efficiency brought about production factors low-productivity sectors high-productivity sectors. replacement factors brought about industrial structure upgrading important factor explain regional economic growth differences Shudan Wang, Cheng, al,2024 Another perspective transformation industrial structure intermediary between technological innovation economic growth, According research, which promote transformation industrial structure, which affects economic growth. theory called technological relevance determinism. impact scientific technological innovation industrial structure primarily constituent element productivity.

Scientific technological innovation promote technological progress, which

affects allocation conversion efficiency production elements input output aspects, promoting reform industrial structure al,2025 worth noting upgrading industrial structure threshold effect productivity Wang, ,2024) panel threshold model

analysis

study shows economic growth effect threshold: technological innovation industrial structure upgrading matched coordinated effectively promote growth. means simple industrial restructuring without foundation technological innovation fails achieve expected growth effect, temporarily suppress economic growth transformation costs.

Based provincial quarterly China, scholars constructed global vector autoregressive model analyze dynamic impact technological innovation industrial structure upgrading regional economic growth. believe technological innovation promote economic growth, whereas industrial structure upgrading heterogeneous different regions Hongli 2025; Yumei al,2025; al,2025) Simultaneously, scholars spatial econometric model analyze impact different technological innovation models regional economic development technological innovation regional economic development significant spatial correlation Siming ,2021; al,2020) spatial correlation means impact scientific technological innovation upgrading industrial structure limited local areas radiation effect surrounding areas through knowledge spillovers, industrial linkages other channels Mahmood Ahmad al,2020) Coupling coordination theory provides perspective analyzing interactive relationship between scientific technological innovation upgrading industrial structure constructing evaluation index system, scholars analyzed coordinated development regional scientific technological innovation industrial structure upgrading based entropy

method

coupling coordination model al,2025; al,2024) research found there coupling relationship between scientific technological innovation upgrading industrial structure, which interdependent mutually reinforcing mainly realized through interaction

factors, demand structure environment ,2024; Yang, ,2023; ,2024, Zhao, al,2025 scholars focus high-tech manufacturing industry series study technological innovation positive impact industrial resilience, industrial structure plays mediating process, technological innovation indirectly enhances industrial resilience promoting industrial upgrading [38]; Other scholars studied impact technological innovation product innovation economy perspective innovation heterogeneity, using industrial structure mediator confirm types innovation promote economic growth regional stage heterogeneity. proposed industrial structure upgrading needs coordinated improvement factor markets breakthroughs green innovation effectively promote low-carbon transformation Based this, study explores three aspects: firstly, analyzing causal relationship between industrial structure technological innovation, helping clarify impact mechanism effectiveness economic growth; Secondly, existing empirical studies mostly provincial spatial panel data, technological innovation indicators mostly described overall level. subdivided, based entire industry, without constructing industry-specific indicators, resulting vague

conclusions

difficulty clarifying synergy spillover effects innovation between industries.

Therefore, study believes necessary explore heterogeneity interactivity impact innovation different industries industrial structure through industry-specific technological innovation indicators, whether affect transmission mechanism industrial structure economic growth; Thirdly, existing research analyzes impact technological innovation through industrial structure,

analysis

industrial structure upgrading clear enough. mostly refers definition Chunhui construct indicators industrial rationalization upgrading, proportion tertiary industry measure industrial upgrading, without considering reality China still dominated secondary industry.

Therefore, study adjustments industrial structure indicators.

analysis

perspective micro-enterprises, study explores relationship between technological innovation, structural transformation economic growth. advantage macro-research clearly decomposed interactive relationship between variables.

Micro-research measurement

methods

determine causal relationship between technological innovation, industrial structure economic growth, analyze transmission mechanism technological innovation industrial structure through intermediary threshold effect basis, certain policy analys

3.1 Model

Setting

3.1.1 Principal

effect regression Figure Effect Logic Diagram believe scientific technological innovation driving force behind industrial structure, which turn, impact economic growth. determine relationship among three variables, return steps determine causal relationship between three variables.

3.1.2 Regression

Intermediary Effect After clarifying relationship between three, transmission mechanism scientific technological innovation industrial structure deeply analyzed, intermediate variable found: capacity utilization rate. relationships between three parameters expressed follows Figure

mediating mechanism, study draws Abbott(2017) Wilkinson(1979) "stepwise regression" estimate intermediate variables.

Specifically, benchmark regression tested capacity intermediate variable, research model constructed coefficients (1),. significant, intermediate variable intermediate. coefficient significant, indicates mediating effect local, versa; important, which means mediating variable plays mediating role. coefficient significant, indicates mediation effect partial mediation effect otherwise complete mediation effect.

3.1.3 Interaction

model study constructs interaction (structure industrial structure technological innovation simultaneously distinguish synergistic effects regions economic growth.

3.1.4 Threshold

Effect Model

Taking industrial structure threshold variable, model exploring impact technological innovation economic growth follows:

Single-threshold Effect Model

E G it = μ i + β 1 T I it ⋅ I(I S it ≤γ ) + β 2 T I it ⋅ I(I S it > γ ) + δ X it + ε it (4)

Where: economic growth level region/industry Individual fixed effects, controlling time-invariant heterogeneity region/industry level (e.g., geographical location, institutional environment, etc.); Technological innovation variable (e.g., investment, patent output, etc.); Industrial structure variable (serving threshold variable measure degree industrial structure upgrading); threshold value estimated; Indicator function (taking condition satisfied, otherwise Vector control variables (e.g., capital stock, labor input, etc.); Coefficient vector control variables; Random error term.

Double-threshold Effect Model there double threshold industrial structure economic growth, model extended Where threshold values estimated, dividing industrial structure intervals capture heterogeneous impact technological innovation economic growth respectively.

3.1.5 Difference-in-Differences

(DID) Model Taking supply-side structural reform (SSR) example, distinguishing between "high-tech enterprises (treatment group)" "traditional enterprises (control group)", model exploring policy effects Treate (Treate Where:

Treate Dummy variable treatment group (taking high-tech enterprises, traditional enterprises); Dummy variable policy (taking later, otherwise); Treate Interaction term, whose coefficient measures policy treatment effect impact supply-side reform high-tech enterprises relative traditional enterprises); Individual fixed effects (controlling time-invariant heterogeneity enterprise level); fixed effects (controlling common shocks annual level, e.g., macroeconomic fluctuations); Constant term; other variables defined before.

3.2 Data

Sources Variable Selection research sample study listed companies relevant listed companies China Taian (CSMAR) database annual reports relevant companies obtained Shenzhen official website Shanghai Stock Exchange Simultaneously, study performs reduction non-ratio continuous variables reduce impact outliers. study eliminates disclosed annual report enterprise.

3.2.1 Definition

and Selection of Variables

1. Explained

variable: economic growth enterprise level, business growth often alternative indicator macro-GDP. addition, growth economic growth robustness regression. collected

2 China

Taian (CSMAR) database

3 Shanghai

Stock Exchange

Based 2006, capita value calculated after excluding impact price fluctuations through deflator province (autonomous regions municipalities), annual growth obtained.

2. Explanatory

Variables: Industrial Structure Technological Innovation space limitations, chapter, ratio tertiary industry added value secondary industry added value industrial structure indicator measure service level advanced level regional industrial structure. enterprise level, proportion service industry enterprise supplementary alternative variable robustness testing. scientific technological innovation, amount investment (RDSpendSum) total number patents granted selected proxy indicators scientific technological innovation, which reflect intensity resource input output

results

innovation activities respectively.

3. Intermediary

variable: capacity utilization enterprise production capacity utilization (actual production capacity/design production capacity) calculated using industry research annual corporate report data, regional average value taken measure resource allocation efficiency production factor utilization level. variable connecting "factor input-output efficiency, capacity utilization plays intermediary industrial structure technological innovation affecting economic growth. upgrading industrial structure improves adaptability resources eliminating backward production capacity developing value-added industries, scientific technological innovation improves utilization efficiency equipment through technological innovation technological iteration, which achieve economic growth through optimization production capacity utilization (Hsieh Klenow, 2009)[5].

4. Control

variables: Characteristic index system listed companies.

Since independent variables study include scientific technological innovation industrial structure, endogenous problems inevitably occur these variables regressed, chosen control variables possible reduce endogenous variables.

There three specific categories Indicators corporate governance dimensions: (whether chairperson board concurrently, concurrently). reflects checks balances corporate governance

structure, which, turn, affects decision-making innovation investment capacity allocation. shareholding ratio largest shareholder) measures ownership concentration.

Excessive shareholding ratio "tunnel effect" reduce enthusiasm minority shareholders supervise management (Shleifer Vishny, 1986) resulting short-term investment bias. reflecting decision complexity. scale large higher communication costs inefficient decision-making (Yermack, 1996) especially technology-intensive industries, where negatively correlated conversion investment. model incorporated ensure integrity governance structure analysis.

Indicators corporate finance growth dimensions: Size, company size, logarithmic total assets (return total assets) (Return Equity) two-dimensional indicators measure corporate profitability; reflects efficiency asset operations, reflects level shareholder returns Cflow ratio operating activities) reflects liquidity constraints. shortfalls limit corporate investment equipment refreshments (Almeida 2004).

Finlev, measure financial leverage, (Total Assets Growth Rate) measures growth enterprise.

Fast-growing enterprises suffer decrease utilization rapid expansion production capacity (Penrose, 1959).

Enterprise Characteristics Market Dimensions: (Listing Period) reflects cycle enterprise.

Long-listed

companies may face a technology path lock-in (Arthur, 1989).

TobinQ (Enterprise Value Ratio) measure market valuation expectations.

According theory (Tobin, 1969), TobinQ companies increase capital expenditure research development investment.

SOE(1 state-owned enterprises), reflecting differences nature property rights.

State-owned enterprises advantages credit acquisition policy subsidies, there principal-agent costs (Zhang Weiying, 1995).

3.2.2 Descriptive

statistics kurtosi ariable edian ariance (Total Assets Growth Rate) IndependentDirectorNu capacity utilization study included observational samples covering multidimensional variables economic structure, corporate finance corporate governance.

section distribution representative certain extent, which provides solid foundation subsequent

analysis

Table

3.3 Benchmark

Regression Robustness

3.3.1 Regression

Impact Technological Innovation Industrial Structure Through simple regression, control variables added Table Table Regression

Results

Investment Industrial Structure industrial ratio 1.575 intercept (7.704) 0.000 (3.903) (-1.399)

C ontrol variable =YES

economic economic growth growth intercept industrial structure Industrial Structure-Service (0.914) 0.000* (2.303) (0.421) Economic growth (proportion enterprises serviced) Economic growth

test F(13,11429)=114.619,p=0.000 F(13,11471)=2.901,p=0.000

Note: signify significance level, respectively; statistics brackets

results

investment scientific technological innovation driving force promoting upgrading industrial structure Table consistent theoretical logic technological innovation promotes industrial upgrading through vertical innovation model", investment promotes industrial transformation value-added direction developing products improving technological efficiency.

3.1.2 Return

Company's Industrial Structure Operating Growth Table Regression

Results

Corporate Industrial Structure Business Growth Economic growth (secondary industrial structure)

F(1,12230)=88 F(11,11431)=109.027, F(11,11432)=635.460, F(11,11432)=654.362, F(11,11432)=529.970, 3.211,p=0.000

C ontrol variable =YES

Note: signify significance level, respectively; statistics brackets First, regression industrial structure business growth analyze impact industrial structure economic growth enterprise level Table coefficient positively significant. square industrial structure continues regressed, coefficient still positive significant primary significant secondary joint primary secondary still significant.

Therefore, impact industrial structure economic growth simple linear relationship. illustrate robustness results, columns indicator economic growth replaced actual indicator industrial structure proportion enterprise services. coefficient industrial structure still positive, indicating model effective explaining variation within individuals.

result

confirms positive effect industrial structure upgrading economic growth stable change adjustment dimension calculation

method

variables. estimation deviation caused selection variables excluded.

3.4 Fixed

Effect Model

analysis

relationship between scientific technological innovation industrial structure economic growth, fixed-effect model

analysis

based enterprises solve endogenous problems Table Therefore, section focuses comparing

results

under fixed effect random effects, -fixed effect bidirectional fixed effects.

Variable model model fixation effect Bidirectional fixation effect

Variable model model fixation effect Bidirectional fixation effect 12.4617 6.1895 intercept (1.8239) (14.0632) (9.1115) (1.8811) 2.1969 3.1792 1.1377 2.0944 industrial structure (9.3994) (37.0889) (16.3181) (9.7194) F(12,11463)=340.0918,p=0

χ 2(12)=2105.9695,p=0.0000 F(12,14405)=25.1480,p=0.0000 F(11,11449)=9.5209,p=0.0000

C ontrol variable =YES

model eliminate problems caused heterogeneity (such geographical location institutional environment) change individual provincial level model assumes individual differences random unrelated explanatory variables. fixed -time effect controls impact common shock missing macro variables. two-way fixed effect controls individual effects contains information about missing variables. regression

results

two-way fixed effect close Table impact industrial structure economic growth always positive significant.

Through variable substitution multi-model setting, problems endogeneity robustness effectively alleviated, fixed effect model solves missing variable deviation caused individual heterogeneity Variable substitution eliminates interference measurement error, multi-model comparison verifies reliability results.

Table phenomenon negative while within positive explained nature fixed effects model variation decomposition panel data. noted, model eliminates interference time-invariant heterogeneity focusing within-individual (intra-entity time) causal relationship.

Specifically, reflects model ability explain total variation contrast, within captures explanation within-individual time-varying variation. model absorbs time-invariant individual heterogeneity remaining between-individual variation explained, leading negative However, within being positive indicates within entity, time-varying explanatory variables (lgRD proxying technological innovation) effectively explain temporal changes dependent variable aligns model advantage addressing individual heterogeneity focusing intra-entity dynamic relationships, corroborates subsequent

conclusion

fixed effect model solves omitted variable caused individual heterogeneity.

Table

Summary

Panel Regression

Results

Fixed Effects Innovation Industrial Structure 4.241 3.729 intercept (21.738) (22.268) 0.079 0.112 (4.522) (7.951) F(10,11433)=94.023,p=0.0

C ontrol variable =YES

3.5.1 Regional

level Bidirectional fixation effect 1.122 0.824 (5.649) (3.499) 0.121 0.192 (6.337) (9.708) F(10,14374)=14.044,p=0.0 F(10,11418)=2.430,p=0.0 Variable model model fixation effect

χ 2(10)=984.183,p=0.0

3.5 Grouping

Regression: Heterogeneity

Analysis

positive impact scientific technological innovation economic growth confirmed, whether transformation industrial structure promote economic growth always needs practical verification.

Through aforementioned regression, obtain positive impact between industrial structure economic growth, section explore whether

there is heterogeneity in the magnitude of this impact .

research status Chapter evident there strong imbalance industrial structure, technological level development status different regions, especially eastern developed regions developed regions central western regions, where industrial transformation process completely different.

Therefore, heterogeneity regional level studied. Considering uneven level economic development, study Aizhen (2023) regional sample classification. provinces (excluding Tibet) sample divided three regions: east, middle regression analysis. specific provinces divided follows: eastern region includes provinces Beijing central region includes provinces Hubei western region includes provinces Yunnan. division derived classification China's national regions since 1986,

eastern region including provinces (cities) including Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong Hainan; central region includes provinces (regions) including Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan Guangxi; western region includes provinces (regions) including Inner Mongolia, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang Chongqing.

Table Regional Regression

Results

Industrial Structure Economic Growth Group regression Variable entirety "East" "Central" west" 12.344 11.818 10.780 20.151 constant (14.148) (11.549) (4.970) (8.096) 3.126 3.009 3.753 2.717 industrial structure (37.122) (28.057) (22.291) (11.483) variance ratio F(11,14421)=201.564,p=0.000 F(11,11375)=121.613,p=0.000 F(11,1976)=67.368,p=0.000 F(11,1046)=23.920,p=0.000

p<0.05 *p<0.01

C ontrol variable =YES

above

results

Table industrial structure coefficient region positive significant, lowest number samples western region. transformation central region behind, economic brought about transformation industrial structure smallest. limited difficulties faced resource-based industries, infrastructure -quality urbanization. early stage transformation, tertiary industry mainly based productive services, focusing backward production capacity. level innovation difficult drive economic growth. eastern region developing steadily industrial structure coefficient close national level. transformation western region robust improve economy Table region highest coefficient. related development western region opening-up policy Initiative recent years.

Industrial transformation strongly supported effect obvious.

Structure Region

Group regression Variable entirety "East" "Central" west" 0.167 constant (-0.688) (-0.528) (5.973) (-0.759) 1.868 2.496 0.076 0.709* (4.973) (4.842) (25.979) (2.485) variance ratio F(11,14478)=9.462,p=0.000 F(11,11367)=9.078,p=0.000 F(11,1990)=87.823,p=0.000 F(11,1097)=2.367,p=0.007

p<0.05 *p<0.01

C ontrol variable =YES

Heterogeneous Grouping Regression Model Science Technology Innovation Industrial Structure Table Table Funding Grouping Regression Group regression model Variable entirety "East" "Central" west" 0.5127 0.5373 0.4538 0.4844 constant (13.7723) (12.0149) (6.3291) (3.9834) 0.0000 0.0000 (2.7293) (2.7152) (0.9281) (0.8851) F(11,14491)=138.1815,p=0.0000 F(11,11379)=102.6697,p=0.0000 F(11,1991)=32.0317,p=0.0000 F(11,1097)=12.8270,p=0.0000

p<0.05 *p<0.01

C ontrol variable =YES

carry robustness regression reduce error caused funding characterization scientific technological innovation, study adopts

method

replacing explanatory variables carry robustness regression.

Replace technological innovation total factor productivity continue return economic growth. overall picture remains significant.

3.5.2 Industry

Stratification Study Similarly, studies heterogeneity between science technology innovation industrial structure.

Therefore, division standard between high-tech traditional industries considered.

According grouping regression

results

Table Table Industrial Structure Economic Growth Return Sector

Grouping regression model Variable entirety manufacturing industry service sector 18.537 17.475 23.343 constant (20.639) (17.210) (12.315) 0.005 0.005 5.701 industrial structure (2.145) (2.212) (8.037) variance ratio F(11,14421)=68.981,p=0.000 F(11,11853)=53.535,p=0.000 F(11,2556)=25.650,p=0.000

p<0.05 *p<0.01

C ontrol variable =YES

Structure Industry Grouping Regression Model-Simplified Format Variable entirety manufacturing industry service sector constant (-1.503) (-1.714) (-0.215) 0.065 0.126 0.003** (percentage) (5.130) (6.603) (21.885) variance ratio F(11,11315)=9.154,p=0.000 F(11,9348)=11.233,p=0.000 F(11,1955)=80.513,p=0.000

p<0.05 *p<0.01

C ontrol variable =YES

overall model, TFP_OP coefficient 0.0369**(t=6.4953), indicating every increase total factor productivity, return total assets (ROA) increases 3.69% average, relationship significant level Table Table expectation endogenous growth theory (TFP) comprehensive embodiment technological progress resource allocation efficiency, whose promotion directly improve corporate earnings reducing increasing product added value.

Among control variables, coefficient 1.1997(t=21.4030) confirms self-strengthening effect profitability, while Finlev(0.1377) shows positive effect moderate reflecting synergistic effect capital structure optimization.

Industrial Structure Grouping Regression Model-Simplified Format

Variable 1.946 2.768 3.604 constant (22.981) (13.104) (4.303) 0.000* 0.006 (2.192) (8.463) (1.145) F(11,14133) variance F(11,1919)=30.97 F(11,110)=2.26 =76.451,p= ratio 8,p=0.000 0,p=0.016

p<0.05 *p<0.01

C ontrol variable =YES

2.303 2.286 (2.400) (3.427) (-1.887) 0.042 (-1.951) (3.541) (-0.125) F(11,208)=3.73 F(11,210)=5.49 F(11,8)=2.009 7,p=0.000 1,p=0.000 ,p=0.166 entirety public utilities business industry estate comprehensive finance 1.764 (18.797) 0.000 (2.427) F(11,11618)=49.8 19,p=0.000

1. Public

utilities: efficiency dividend released significantly TFP_OP coefficient 0.0363*(t=2.2179), significant level. technological innovations public utilities (e.g. power water) mostly focused intelligence infrastructure (e.g. smart smart water system), which directly reduce operation (e.g. decrease corresponds saving). addition, industry natural monopoly attribute, advantage brought about increase easily converted profit; example, energy power station increases power generation efficiency through digital operation maintenance, correspondingly increases approximately percentage points.

Among control variables, Finlev coefficient 0.2113**(t=3.9800) indicates public utilities financing promote technological transformation, whereas coefficient of-0.0041(t=-1.5737) significant, indicating industry technology iteration affected enterprises (owing infrastructure renewal cycle).

Business: Failure Drive Profits TFP_OP coefficient 0.0046(t=0.1062), which significant. business sector (wholesale retail), increase mostly supply chain optimization (e.g. logistics digitalization) business model innovation (e.g., e-commerce platform), innovation easily offset competition imitation example, supermarket chain's investment intelligent sorting system resulted logistics price followed peers resulted increase gross profit margin. addition, commercial enterprises light asset model contribution capital efficiency while marginal impact human efficiency improvement (such intelligent scheduling systems) weak.

Among control variables, cflow coefficient-1.6375(t=-2.1141) indicates tight flows inhibit earnings, while coefficient-0.0757(t=-2.5230) indicates expansion management scale commercial enterprises likely inefficient decision-making offset potential benefits

3. Industry:

TFP's manufacturing dividend persists coefficient TFP_OP 0.0372**(t=5.6933), which close entire model. promotion industry (especially high-tech manufacturing) directly related technological innovation (such industrial robot substitution digital design), which reduce rejection percentage points 4-6%. example, through transformation flexible production lines, automobile manufacturing enterprises increased units 5.6%.

Among control variables, coefficient 1.2829(t=15.5438) stronger overall level, reflecting scale effect industrial enterprise earnings Tobin coefficient 0.0078(t=2.6902) indicates market valuation plays significant guiding investment decisions industrial enterprises.

High-value enterprises inclined invest resources technological upgrading. estate: inverse relationship between earnings TFP_OP coefficient-0.0177(t=-0.4229), though significant, negative. improvement estate industry mostly depends efficiency development (such shortening project cycle) construction technology (such fabricated buildings) however, accounts efficiency improvement cannot offset increase price. example, estate company shortened construction cycle through technology, price increased during period, resulting percentage point decrease addition, industry fully consider impact fluctuations housing prices. market decreases, efficiency improvements cannot hedge against decline sales price.

Among control variables, coefficient-0.0583(t=-0.6652) shows profit efficiency state-owned enterprises estate field lower private enterprises, while cflow coefficient-0.8485(t=-1.7635) close significant, which reflects tight estate enterprises suppresses profit Synthesis: diversified synergistic effect outstanding coefficient TFP_OP 0.0697* Table which highest among industries. enhancement integrated industries (business-diversified enterprises)

results

ability integrate resources (such sharing technology platforms across business lines). example, integrated group integrated retail logistics business through digital China Taiwan, resulting increase units increase 4.3%. marginal contribution earnings industry super-industrial, diversification spread technological innovation. business loses money short term, earnings other businesses smooth overall Among control variables, TobinQ coefficient 0.0558(t=2.7306) indicates market higher valuation premium technology input integrated enterprise, while coefficient of-0.0130(t=-3.5317) indicates technology -locking problem established integrated enterprise serious, young enterprise improve

Group regression 12.344 15.231 constant (14.148) (6.278) (1.636) 3.126 4.106 2.714** industrial structure (37.122) (16.874) (3.191) F(11,14421)=201.

F(11,1953)=43.8 variance ratio 564,p=0.000 25,p=0.000 6,p=0.005

p<0.05 *p<0.01

C ontrol variable =YES

process Table Table

Results

Intermediation

Summary

Intermediation

Results

Action Total effect

capacity utilization =>GDP

economic growth

Reciprocal_ total number of patents = >

capacity utilization =>GDP economic

growth Remarks:

p<0.05 *p<0.01

Bootstrap type = percentile bootstrap

method

11.943 12.162 (12.113) (0.147) (2.826) (1.373) 3.098 2.873 3.658** (32.581) (6.580) (7.882) (0.723) F(11,11851)=155.

F(11,212)=10.1 F(11,220)=11.3 F(11,10)=1.92 319,p=0.000 17,p=0.000 42,p=0.000 1,p=0.157 (Boot Direct

conclusion

value) value) BootCI) effect Partial 3.2490 intermediation Partial 2.3682 intermediation Variable entirety public utilities business industry estate comprehensive finance F(11,115)=2.65 After clarifying relationship between scientific technological innovation industrial structure growth, study further explore transmission mechanism scientific technological innovation. found utilization production capacity intermediary variable transmission Intermediate effect value 3.5032 0.0125 20.3182 2.4857 0.0058 20.3182 first industrial structure capacity utilization transmission economic growth. total effect coefficient industrial structure economic growth 3.5032, among which intermediate

industrial structure change causes change capacity utilization units, capacity utilization change affect units change, intermediate effect value a*b=0.2542.

Accounting 7.25% total effect, their products significant, indicating intermediate effect established; other words, transformation industrial structure changes economic growth adjusting utilization production capacity. addition, there direct effect c'=3.2490, indicating industrial structure affects economic growth through non-production capacity utilization paths (such technology spillovers upgrading demand structure).

Second, transmission scientific technological innovation capacity utilization economic growth shows that: total effect science technology innovation (total number patents) growth intermediate effect which significant.

Therefore, ability production, profit profit important intermediate variable transmission mechanism scientific technological innovation, enterprise structure transformation economic growth. plays intermediate links innovation structural transformation structure transformation growth explains government attached great importance overcapacity problem. unblocked, supply adjustments become ineffective.

3.7 Interaction

Effects Based theoretical

analysis

macro-empirical research, determine whether impact micro-level industrial structure economic growth affected current level science technology.

Therefore, interaction between industrial structure investment model Table Table Regression

Results

Interaction Effects Variable Industrial structure research Industrial Structure Patents development investment 53.908 35.881 intercept Interaction 0.701** R2(within) Sample

test F(10,11446)=167.366,p=0.000 F(10,11446)=230.191,p=0.000

C ontrol variable =YES

interaction effect confirms theory "structure-innovation synergy driving": upgrading industrial structure provides institutional industrial basis efficient release research development investment, while research development investment injects continuous power upgrading industrial structure, which forms positive feedback loop. specific performance follows:

Optimization factor allocation efficiency: industrial structure changes service industry high-tech manufacturing industry, demand high-level factors, knowledge technology, increases, marginal output research development input increases accordingly.

Industrial adaptability technological innovation: traditional industry-led industrial structure, research development investment mostly process improvement (such efficiency improvement steel smelting) limited marginal contribution; However, leading structure service industry, investment focuses model innovation technological breakthrough, economic pull-up effect shows geometric growth.

3.8 Threshold

Effect study analyzes whether there threshold effect scientific technological innovation industrial structure industrial structure economic growth.

1. Double

Threshold Effect Industrial Structure Economic Growth Table Table Based Hansen threshold model test, industrial structure double threshold effect economic growth (single threshold significant; double threshold close level significance, combined Bootstrap threshold comparison, supports existence double threshold). thresholds (-0.264 0.469) divide industrial structure level three intervals: range: industrial structure level (industry dominated, service industry seriously lagged); Intermediate range:-0.264 industrial structure level (coordinated development industry service industry) range: industrial structure level (dominated service industry, proportion industry declining continuously).

Table Threshold Effect (Industrial Structure Economic Growth)

square Threshold squares error statistics value threshold threshold threshold (RSS) (MSE) Single threshold Double threshold range, industrial structure weakest insignificant effect economic growth. stage, economy dominated traditional industries (such steel), service industry mostly low-end supporting (wholesale retail, basic logistics), which cause problem "insufficient quality structural upgrading. middle range, industrial structure strongest effect economic growth significant coefficient, which characterized "accelerated release structural dividend stage, industry remained competitive (high-end manufacturing, equipment manufacturing), service industry transformed producer services design, supply chain management) consumer upgrading services (medical, health, cultural entertainment). simultaneous development industries formed synergistic mechanism: pulling economy production demand forming positive demand-supply cycle. range, promotion effect industrial structure economic growth significant negative direction implies "structural hollowing risk. proportion service industry high, industrial excessively weakened, innovation chain fractured, employment structure unbalanced. shrinking industry demand intermediate goods, growth service industry "industrial engine Therefore, threshold effect industrial structure economic growth reveals cause development two-way coordination factor allocation, technological innovation supply demand Industrial structure transformation should consider joint development industry service industry, instead abandoning strongly support other.

Model information details Regression

method

Fixed-effects (within)

regression Interpreted variable Operating growth ratey Grouping variable Observations group

Prob > F 0.0000

industrial industrial industrial

Variance parameter/test numerical value explain effect

variance to total variance ( ρ =

(allu F(79, 948)=1.29 whether individual effects

are combined significantly, Prob >

1. The

Threshold Effect Technological Innovation Industrial Structure

results

based Hansen threshold model there significant single threshold effect impact technological innovation industrial structure (single threshold rejecting threshold" hypothesis Table Double threshold support existence double threshold), threshold threshold divides level scientific technological innovation areas: level scientific technological innovation equal 0.027, intensity input patent output still weak, which enough drive transformation industrial structure; level technological innovation greater 0.027, investment patent transformation break through critical value, effect technological innovation restructuring industrial structure significantly enhanced.

Table Threshold Effect square Threshold squares error statistics value threshold threshold threshold (RSS) (MSE) Single threshold

square Threshold squares error statistics value threshold threshold threshold (RSS) (MSE) Double threshold technological innovation level range, technological innovation activities mainly based adaptive improvement traditional industries, which difficult break through solidified pattern industrial structure. time, research development resources mostly invested process optimization areas obvious short-term benefits, local upgrading equipment traditional manufacturing industry, insufficient support extension industrial value chain Non-core technologies, design accounted patent output stage, which could provide support industrial transformation technology. elasticity coefficient technological innovation industrial structure upgrading 0.08, indicating linkage mechanism between technological innovation industrial structure effectively established. technological innovation level breaks through threshold enters high-level range, driving force technological innovation industrial structure presents nonlinear characteristic "quantitative change qualitative change. formation threshold effect

results

synergy three mechanisms. First, there "accumulation threshold" effect technological innovation, low-level investment achieve "point improvement" about increase production efficiency, which cannot shake fundamentals industrial structure; After investment high-level segment exceeded critical value, technological innovations industrial Internet platforms formed "network effect" connecting million units equipment, promoted systematic restructuring industrial organization. example, after investment intensity Shenzhen exceeded 2019, value-added ratio strategic emerging industries increased Second, there "ecological threshold" constraint industrial synergy, innovation ecology low-level regions perfect example, density technology trading market million people), sporadic technological breakthroughs difficult transform driving force structural upgrading; density high-level interval patent transformation intermediaries exceeds 5/10,000 people, forming complete chain "research development pilot industrialization". example, Beijing's Zhongguancun relies scientific research cluster universities proportion science technology service industry service industry Third, there "trigger threshold" effect policy incentives. subsidy insufficient low-level range example, subsidy amount

investment enterprise), enterprise inclined short-term production investment, expense deduction exceeds 175%, enterprise innovation revenue enterprise increases 30%-50%. example, after income exemption high-tech enterprises increased 2018, output value high-tech manufacturing industry industrial enterprises increase percentage points annually.

Continued Table Regression

Results

_cat#c.TFP_OP _cat#c.TFP_OP _cat#c.TFP_OP Regression

method

Fixed-effects (within) regression Interpreted variable Industrial structurex Grouping variable

Observations group

Prob > F 0.0000

Variance numerical value explain parameter/test Proportion individual effect variance total variance

( ρ = σ /( σ + σ ))

whether individual effects combined

F(79, 948)= ...

significantly (Prob model

F test (allu = 0)

(Original whole, which supports existence individual fully shown) effects) perspective policy practice, low-level regions implement "innovation foundation+ecological cultivation" two-wheel drive strategy: through establishment "innovation threshold subsidy" example, subsidy 500,000 every enterprise research development investment), intensity exceed 2.5%; time, regional technology trading center established based standard million people. 2023, pilot regions central western regions increase patent conversion

High-level regions strengthen "innovation-industry" coordination mechanism, establishment industrial chain innovation alliances fields semiconductors biomedicine. alliances Yangtze River Delta region increased investment efficiency member enterprises while optimizing allocation innovative elements 2022, Greater attracted 50,000 high-level overseas talents through special talent policy, which increase proportion strategic emerging industries 3.9DID differential study takes supply structural reform policy shock, high-tech enterprises treated group low-tech enterprises control group, double-difference

method

(DID) identify causal effect policies investment (RDSpendSum). terms policy background, November 2015, central government proposed major tasks "eliminating production capacity, eliminating inventory, deleveraging, reducing costs, supplementing shortage boards", among which "supplementing shortage boards" directly refers upgrading high-tech industries, supporting policies increasing proportion research development expenses deductions (from 175%), supporting strategic emerging industries, etc., create innovation incentive environment high-tech enterprises.

Table

Summary

Model

Results

model

summary

Effect value RDSpendSum (amount research Standard error development investment) Control Treated (experimental Before group) Diff( 0.0000 Control Treated (experimental After group) Diff( 0.0000 Diff-in-Diff 0.0000**

Note: R2 = 0.0687, adjust R2 = 0.0684

p<0.05 *p<0.01

Before reform, average investment experimental group yuan, control group yuan, inter-group difference (diff) Diff)97341590.17 (t=7.64**), which indicat investment high-tech enterprises already significantly higher low-tech enterprises, which consistent expectation factor endowment theory high-tech industries naturally drive Table

After reform, investment experimental group increased RMB251658673.56, investment control group increased RMB65261295.83, increased RMB186396747.73 (t=25.27**), reflecting further widening investment between groups after implementation policy.

Differential effect policy increase (t=6.05**) investment high-tech enterprises compared control group, accounting 91.5% difference between groups before reform, indicating marginal incentive effect supply reform investment high-tech enterprises significant Table Table Regression

Results

regression

results

coefficient regression 39199500.5589 constant (5.7734) 97341590.1653 High-tech industries (TFP) (7.6400) 26062425.2661 (3.1052) 89055157.5693 High-tech industries (TFP) (6.0489) Adjust

F F(3,11402)=280.2666,p=0.0000

Note: Interpreted variable RDSpendSum (amount research development investment)

p<0.05 *p<0.01

result

confirms "policy guidance-resource reallocation" theory: supply reform pushes factors production low-efficiency sector (low-tech industry) high-efficiency sector (high-tech industry) through mechanism administration market. specific performance follows: "De-production" forces low-tech enterprises reduce traditional production capacity investment growth research development investment sluggish; incentives (such research development expenses deductions) credit incentives complement "short board" reduced marginal research development costs high-tech enterprises 12%-15%, freeing space innovation investment.

Taking manufacturing industry example, growth investment high-tech manufacturing industry increased 12.3% after 2015, whereas growth traditional manufacturing industry decreased 3.1%, which consistent

results

supply reform significantly increased investment intensity high-tech enterprises through mechanism "doing subtraction" eliminate backward production capacity) "doing addition" supplement

innovation short board), policy effect continuous. provides empirical evidence "policy-led innovation": strategic emerging industries, combined policy "research development expenses deduction+special support" continued, deduction ratio increased 200%; Establish transformation guide low-tech enterprises, direct revenue production capacity technological transformation traditional enterprises through mechanisms "capacity replacement index trading"; Improve financing facilities high-tech enterprises, expanding industry coverage science technology innovation board lowering listing threshold innovative small medium-sized enterprises. study provides quantitative decision-making basis deepening supply reform building innovation-driven development pattern "14th Five-Year Plan" period.

conclusions

prospects purpose study clarify important scientific technological innovation transformation industrial structure economic growth provide policy recommendations. issue discussed using macro- enterprise-level data.

conclusions

study constructs general equilibrium model includ industrial structure technological innovation government's macroeconomic management level. focuses current situation problems China's supply structural reform development, current situation, problems mechanisms China's technological innovation system supporting supply structural reform, empirical

analysis

simulation prediction technological innovation supporting supply structural reform. explores theoretical value, policy value practical value contains revising, expanding, constructing scientific effective mathematical models, providing theoretical basis decision-making reference government enterprise departments decisions supply structural reform.

4.2 Research

Limitations Prospects Limited availability ability data, paper certain limitations, which mainly reflected research objects research dimensions. terms research objects, owing small sample scientific technological innovation different industries statistical value, research paper distinguish high-tech industries traditional industries level middle-class industries construct industry-advanced indicators. detailed indicators regional level obtained future, interactive impact innovation effects between different industries analyzed explained carefully, detailed indicators malicious purchase plans constructed.

Second, because effective length, study depict analyze other aspects industrial structure indicators, industrial structure transfer perspective global value chains.

Exploring upgrading rationalization industrial structure transformation context globalization requires further exploration research. research dimension, study focuses driving force scientific technological innovation supply industrial structure transformation, mainly focusing driving force industrial structure adjustment supply side.

However, adjustment industrial structure closely related demand-side drive. demand-side drive changes scientific technological innovation, forming demand-driven innovation-structural adjustment-economic growth cycle.

Supply- demand-side drive often interactive. demand-side supply drive straightened time, study transition process industrial structure transformation under condition coexistence drives better reference value, which become important entry point subsequent research. 4.3Policy recommendations

4.3.1 Strengthen

precise support enterprise technological innovation facilitate transmission technological innovation industrial structure response problem independent innovation capability investment discovered research, firstly, expand policy additional deduction expenses: increase deduction high-tech industries 200%, expand coverage traditional industries

undergoing technological transformation stimulate process upgrading. second promote industrialization patents: establish regional technology trading platform, especially central region where return information system transformation output technological information demand information system upgrading. small medium-sized enterprises, "innovation threshold subsidy" introduced, example, every increase intensity, subsidy provided break through technological innovation threshold (0.027) drives information system transformation.

4.3.2 Implement

differentiated information system adjustment policies based regional industrial heterogeneity economic benefits infrastructure transformation central region lowest, while western region benefits regional differences policy support. central region, production capacity reduction income technological transformation improve production capacity utilization, focus upgrading resource-based industries non-ferrous metals.

Western region: Road" policy dividend develop value-added industries avoid repeated low-end capacity expansion.

Divided industry, high-tech manufacturing productive service industries, establish industry chain innovation alliances enhance TI-IS synergies; traditional businesses, support supply chain digitization public funds reduce competitive imitation losses.

4.3.3 Promote

synergy between supply reform innovation driven policies avoid short-term fluctuations solve problem short-term industrial fluctuations caused previous "three reductions, reduction, supplement" policy, combine capacity reduction support: linking phasing outdated capacity enterprise investment.

Improve financing support high-tech enterprises: expand coverage Science Technology Innovation Board, include innovative small medium-sized enterprises, reduce their financing constraints.

Establish long-term monitoring mechanism "TI-IS-EG" chain, track indicators capacity utilization patent conversion rate, ensure policies promote sustained transformation rather temporary rebound.

Disclosure interests authors disclosed relevant relationships contributions Sanglin :Conceptualization, curation, Formal analysis, Software, Writi original draft, Visualization, Writing review editing, :Funding cquisition, Investigation, Writing review editing, :Validation, Writing review editing,all authors approved final version manuscript. availability available request unding statement study receive funding eferences Zhang, (2025).

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