How Digital Innovation Ecosystems Facilitate Low-Carbon Transformation of the Economy Based on a Dynamic Qualitative Comparative Analysis
Abstract
:1. Introduction
2. Research Framework
2.1. Subjects of Innovation
2.2. Innovative Resources
2.3. Innovation Environment
3. Research Design
3.1. Research Method
3.2. Measurement of Variables
3.2.1. Outcome Variables
3.2.2. Antecedent Conditions
3.3. Data Sources
3.4. Data Calibration
4. Analysis of Empirical Results
4.1. Necessity Analysis of Individual Conditions
4.2. Configuration Analyses for High-Level Low-Carbon Economy
4.2.1. Analysis of Aggregated Results
4.2.2. Between Outcome Analysis
4.2.3. Within Outcome Analysis
4.2.4. Analysis of Non-High Level Low-Carbon Economic Conditions Configuration
4.3. Robustness Tests
5. Discussion
Limitations and Future Research Directions
6. Conclusions and Implications
6.1. Research Conclusion
6.2. Practical Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Tier 1 Indicators | Tier 2 Indicators | Meaning of Indicators |
---|---|---|
Low-carbon economic development | Carbon productivity Y | GDP/carbon emissions |
Innovative subjects | Technological innovative subjects X1 | Number of industrial enterprises above designated size (units) |
Knowledge innovator X2 | Number of general colleges and universities (number) Number of research and development institutions (number) | |
Innovation resources | Talent Resources X3 | Number of employees in the information transmission, computer services, and software industry (million people) |
Financial resources X4 | Investment in fixed assets in the information transmission, computer services, and software industry (billion yuan) | |
Innovation Environment | Digital Environment X5 | Internet broadband access ports (hundred thousand) Mobile switch capacity (ten thousand households) Internet broadband access subscribers (ten thousand) Mobile phone penetration rate (units/100 people) Software business revenue (billion yuan) Total telecoms business (tens of billions of yuan) |
Economic environment X6 | Road passenger traffic (million people) Per capita disposable income (yuan) Gross regional product (yuan) Total import and export of goods (yuan) | |
Technical environment X7 | Sales revenue of new products of industrial enterprises above the designated size (billion yuan) Number of new product development projects (item) Patent authorization (item) |
Variables | Calibration | Descriptive Statistics | |||||||
---|---|---|---|---|---|---|---|---|---|
Fully Affiliated Points | Intersection Points | Fully Unaffiliated Points | Mean | Variance | Min | Median | Max | ||
outcome variables | Y | 1.941 | 0.62 | 0.157 | 0.809 | 0.558 | 0.06 | 0.62 | 2.92 |
conditional variables | X1 | 45,986.05 | 6777 | 582.25 | 12,961 | 14,310 | 335 | 6777 | 70,702 |
X2 | 349.55 | 197 | 37.45 | 201.2 | 92.59 | 33 | 197 | 487 | |
X3 | 47.165 | 6.45 | 0.9 | 12.31 | 16.32 | 0.6 | 6.45 | 101.2 | |
X4 | 609.015 | 130.234 | 24.405 | 193.9 | 190.1 | 0.6 | 130.234 | 1135 | |
X5 | 0.581 | 0.488 | 0.473 | 0.501 | 0.046 | 0.47 | 0.488 | 0.831 | |
X6 | 0.591 | 0.473 | 0.448 | 0.488 | 0.051 | 0.442 | 0.473 | 0.800 | |
X7 | 0.594 | 0.487 | 0.477 | 0.504 | 0.058 | 0.476 | 0.487 | 0.967 | |
Calibration anchors of 0.95, 0.50, 0.05 |
Y | ~Y | |||||||
---|---|---|---|---|---|---|---|---|
Conditional Variables | POCONS | POCOVS | BECONS Adjusted Distances | WICONS Adjusted Distances | POCONS | POCOVS | BECONS Adjusted Distances | WICONS Adjusted Distances |
X1 | 0.807 | 0.851 | 0.036173 | 0.32202 | 0.498 | 0.56 | 0.056269 | 0.298118 |
~X1 | 0.582 | 0.521 | 0.072345 | 0.47153 | 0.867 | 0.828 | 0.088422 | 0.091193 |
X2 | 0.808 | 0.77 | 0.072345 | 0.276017 | 0.58 | 0.59 | 0.052249 | 0.304329 |
~X2 | 0.57 | 0.56 | 0.056269 | 0.494531 | 0.774 | 0.811 | 0.104499 | 0.115261 |
X3 | 0.765 | 0.816 | 0.060288 | 0.299019 | 0.535 | 0.609 | 0.120576 | 0.290952 |
~X3 | 0.634 | 0.561 | 0.180863 | 0.414026 | 0.838 | 0.792 | 0.024115 | 0.113519 |
X4 | 0.743 | 0.801 | 0.265266 | 0.253016 | 0.499 | 0.575 | 0.349669 | 0.304329 |
~X4 | 0.606 | 0.531 | 0.385842 | 0.350772 | 0.828 | 0.775 | 0.160767 | 0.082675 |
X5 | 0.756 | 0.858 | 0.072345 | 0.345022 | 0.474 | 0.575 | 0.180863 | 0.286938 |
~X5 | 0.625 | 0.527 | 0.176844 | 0.408276 | 0.883 | 0.794 | 0.056269 | 0.088176 |
X6 | 0.777 | 0.861 | 0.200959 | 0.258766 | 0.467 | 0.552 | 0.381823 | 0.347804 |
~X6 | 0.596 | 0.511 | 0.313496 | 0.391025 | 0.882 | 0.808 | 0.128614 | 0.069049 |
X7 | 0.748 | 0.886 | 0.088422 | 0.356522 | 0.451 | 0.57 | 0.213017 | 0.294512 |
~X7 | 0.637 | 0.521 | 0.188902 | 0.396775 | 0.91 | 0.794 | 0.028134 | 0.070569 |
Case | Combinations | Indicators | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
case 1 | X4/Y | BECONS | 0.58 | 0.52 | 0.432 | 0.476 | 0.583 | 0.68 | 0.819 | 0.856 | 0.838 | 0.848 | 0.904 | 0.895 | 0.896 |
BECOVS | 0.733 | 0.835 | 0.92 | 0.833 | 0.873 | 0.825 | 0.79 | 0.81 | 0.783 | 0.801 | 0.758 | 0.786 | 0.8 | ||
case 2 | X4/~Y | BECONS | 0.323 | 0.306 | 0.263 | 0.354 | 0.404 | 0.495 | 0.612 | 0.631 | 0.653 | 0.67 | 0.706 | 0.695 | 0.705 |
BECOVS | 0.863 | 0.835 | 0.81 | 0.816 | 0.76 | 0.682 | 0.655 | 0.561 | 0.523 | 0.496 | 0.479 | 0.424 | 0.396 | ||
case 3 | ~X4/Y | BECONS | 0.892 | 0.897 | 0.911 | 0.895 | 0.84 | 0.738 | 0.643 | 0.537 | 0.49 | 0.466 | 0.378 | 0.344 | 0.324 |
BECOVS | 0.383 | 0.431 | 0.461 | 0.512 | 0.528 | 0.563 | 0.599 | 0.607 | 0.622 | 0.643 | 0.613 | 0.619 | 0.636 | ||
case 4 | X6/Y | BECONS | 0.569 | 0.535 | 0.59 | 0.626 | 0.672 | 0.737 | 0.79 | 0.814 | 0.872 | 0.902 | 0.94 | 0.88 | 0.881 |
BECOVS | 0.945 | 0.953 | 0.927 | 0.91 | 0.899 | 0.89 | 0.885 | 0.86 | 0.826 | 0.81 | 0.784 | 0.858 | 0.865 | ||
case 5 | X6/~Y | BECONS | 0.226 | 0.235 | 0.321 | 0.367 | 0.406 | 0.449 | 0.468 | 0.565 | 0.639 | 0.685 | 0.728 | 0.663 | 0.659 |
BECOVS | 0.796 | 0.711 | 0.729 | 0.704 | 0.683 | 0.615 | 0.583 | 0.561 | 0.519 | 0.482 | 0.492 | 0.449 | 0.407 | ||
case 6 | ~X6/Y | BECONS | 0.877 | 0.838 | 0.828 | 0.797 | 0.763 | 0.681 | 0.628 | 0.584 | 0.492 | 0.423 | 0.391 | 0.434 | 0.396 |
BECOVS | 0.349 | 0.391 | 0.457 | 0.488 | 0.505 | 0.521 | 0.516 | 0.588 | 0.614 | 0.632 | 0.639 | 0.65 | 0.648 | ||
case 7 | X7/~Y | BECONS | 0.271 | 0.358 | 0.41 | 0.425 | 0.44 | 0.45 | 0.464 | 0.474 | 0.491 | 0.517 | 0.561 | 0.603 | 0.587 |
BECOVS | 0.801 | 0.727 | 0.713 | 0.672 | 0.659 | 0.631 | 0.602 | 0.548 | 0.521 | 0.498 | 0.506 | 0.451 | 0.403 |
Configuration Path of High-Level Low-Carbon Economic Development | Configuration Path of Low-Level Low-Carbon Economic Development | ||||||
---|---|---|---|---|---|---|---|
Conditional Variables | M1 | M2 | H1 | H2 | D1 | D2 | D3 |
Technological innovation subject X1 | u | u | u | X | X | X | |
Knowledge innovation subject X2 | u | u | u | X | U | ||
Talent resources X3 | u | u | x | X | U | x | |
Funding resources X4 | u | u | x | X | X | ||
Digital Environment X5 | U | U | U | U | X | X | X |
Economic Environment X6 | u | u | x | X | X | X | |
Technological Environment X7 | u | u | u | x | X | x | |
consistency | 0.980 | 0.961 | 0.959 | 0.935 | 0.890 | 0.917 | 0.882 |
PRI | 0.956 | 0.911 | 0.810 | 0.632 | 0.798 | 0.657 | 0.787 |
degree of coverage | 0.604 | 0.569 | 0.401 | 0.354 | 0.608 | 0.392 | 0.656 |
Unique coverage | 0.048 | 0.013 | 0.015 | 0.030 | 0.007 | 0.048 | 0.026 |
BECONS-adjusted distances | 0.020096 | 0.024115 | 0.032153 | 0.048230 | 0.032153 | 0.052249 | 0.024115 |
WICONS-adjusted distances | 0.057504 | 0.092006 | 0.074755 | 0.115007 | 0.224264 | 0.178261 | 0.235765 |
POCONS | 0.933 | 0.886 | |||||
Pooled PRI | 0.856 | 0.791 | |||||
POCOVS | 0.713 | 0.711 | |||||
The number of case frequencies was 5, the original consistency threshold was 0.8, and the PRI threshold was 0.6 |
Regions | M1 | M2 | H1 | H2 |
---|---|---|---|---|
Eastern Region | 0.796727273 | 0.769454545 | 0.532181818 | 0.398454545 |
Central Region | 0.584875 | 0.564625 | 0.526625 | 0.535875 |
Western Region | 0.554090909 | 0.509909091 | 0.483363636 | 0.510545455 |
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Zhang, K.; Wen, Y.; Wu, Y. How Digital Innovation Ecosystems Facilitate Low-Carbon Transformation of the Economy Based on a Dynamic Qualitative Comparative Analysis. Sustainability 2024, 16, 9962. https://doi.org/10.3390/su16229962
Zhang K, Wen Y, Wu Y. How Digital Innovation Ecosystems Facilitate Low-Carbon Transformation of the Economy Based on a Dynamic Qualitative Comparative Analysis. Sustainability. 2024; 16(22):9962. https://doi.org/10.3390/su16229962
Chicago/Turabian StyleZhang, Keyong, Yifeng Wen, and Yunxia Wu. 2024. "How Digital Innovation Ecosystems Facilitate Low-Carbon Transformation of the Economy Based on a Dynamic Qualitative Comparative Analysis" Sustainability 16, no. 22: 9962. https://doi.org/10.3390/su16229962
APA StyleZhang, K., Wen, Y., & Wu, Y. (2024). How Digital Innovation Ecosystems Facilitate Low-Carbon Transformation of the Economy Based on a Dynamic Qualitative Comparative Analysis. Sustainability, 16(22), 9962. https://doi.org/10.3390/su16229962