Dynamic Evaluation of Forest Carbon Sink Efficiency and Its Driver Configurational Identification in China: A Sustainable Forestry Perspective
Abstract
1. Introduction
2. Methods
2.1. Super-Efficient SBM Model
2.2. Malmquist–Luenberger Index
2.3. Spatial Autocorrelation Model
2.4. Dynamic fsQCA
3. Indicators and Data
3.1. Indicator Selection
3.1.1. Evaluation Indicators Selection of FCSE
Input Indicators
Output Indicators
3.2. Influencing Factors and Theoretical Analysis of FCSE
- (1)
- Natural endowment
- (2)
- Social development
- (3)
- Forest management
3.3. Data Sources
3.4. Data Calibration
4. Results
4.1. Efficiency Evaluation for Forest Carbon Sinks in China
4.1.1. Analysis of Forest Carbon Sink Efficiency in China
4.1.2. Spatial Variation Characteristics of Forest Carbon Sink Efficiency
4.1.3. Decomposition of Forest Carbon Sink Efficiency Factors
4.1.4. Spatial Autocorrelation Test of Forest Carbon Sink Efficiency
4.2. Analysis of Driving Factors of China’s Forest Carbon Sink Efficiency
4.2.1. Necessity Analysis
4.2.2. Configuration Analysis
4.2.3. Pooled Results
4.2.4. Between Result
4.2.5. Within Results
4.2.6. Robustness Test
5. Discussion
5.1. Spatiotemporal Characteristics and Mechanism Explanation of China’s FCSE
5.1.1. Efficiency Level and Spatial Distribution
5.1.2. Mechanism Analysis of Insignificant Spatial Autocorrelation
5.2. Driving Path of FCSE: Multiple Concurrent Mechanisms from the Configuration Perspective
5.2.1. Core Variables and Path Heterogeneity
5.2.2. Double-Edged Sword Effect of Disaster Management
5.2.3. Time Effect and Regional Adaptation
5.3. Policy Implications
5.4. Theoretical Contributions and Limitations
6. Conclusions
- (1)
- From 2008 to 2022, the average value of China’s FCSE was 1.1, showing a “high and stable” feature. Technological progress (TC mean 1.21) was the core driving force of sustainable efficiency improvement, but the imbalance of EC among regions restricts long-term sustainability. In terms of spatial distribution, FCSE exhibits a U-shaped gradient pattern with dual peaks in eastern and southwestern regions, showing no significant spatial autocorrelation but demonstrating distinct spatial dispersion characteristics.
- (2)
- Three heterogeneous paths based on the dynamic fsQCA revealed that Configuration 1 involves the coupling of abundant precipitation with economic development, which is suitable for the eastern high urbanization region. Configuration 2 comprises a multi-factor linkage of precipitation, education, pest control, and fire area, demonstrating cross-regional adaptation. Configuration 3 is driven by the combined influence of precipitation and the total value of the primary forestry industry, facilitating the ecological and economic transformation of central China.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Case | Causal Combination | Index | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |
case 1 | X3/Y | Between to Pooled consistency | 0.178 | 0.219 | 0.323 | 0.411 | 0.59 | 0.616 | 0.602 | 0.567 |
Between to Pooled coverage | 0.778 | 0.717 | 0.673 | 0.665 | 0.564 | 0.572 | 0.55 | 0.528 | ||
case 2 | X3/~Y | Between to Pooled consistency | 0.111 | 0.142 | 0.224 | 0.334 | 0.456 | 0.562 | 0.636 | 0.725 |
Between to Pooled coverage | 0.562 | 0.565 | 0.497 | 0.617 | 0.568 | 0.552 | 0.541 | 0.596 | ||
case 3 | ~X3/Y | Between to Pooled consistency | 0.904 | 0.866 | 0.791 | 0.689 | 0.6 | 0.534 | 0.509 | 0.508 |
Between to Pooled coverage | 0.459 | 0.455 | 0.457 | 0.529 | 0.438 | 0.528 | 0.595 | 0.698 | ||
case 4 | ~X3/~Y | Between to Pooled consistency | 0.958 | 0.928 | 0.871 | 0.775 | 0.678 | 0.576 | 0.481 | 0.368 |
Between to Pooled coverage | 0.583 | 0.59 | 0.609 | 0.548 | 0.7 | 0.62 | 0.535 | 0.405 | ||
case 5 | X4/Y | Between to Pooled consistency | 0.321 | 0.353 | 0.428 | 0.488 | 0.591 | 0.561 | 0.546 | 0.552 |
Between to Pooled coverage | 0.838 | 0.798 | 0.726 | 0.737 | 0.624 | 0.616 | 0.589 | 0.611 | ||
case 6 | X4/~Y | Between to Pooled consistency | 0.1 | 0.123 | 0.194 | 0.28 | 0.356 | 0.433 | 0.513 | 0.521 |
Between to Pooled coverage | 0.311 | 0.335 | 0.399 | 0.39 | 0.531 | 0.518 | 0.527 | 0.462 | ||
case 7 | ~X4/Y | Between to Pooled consistency | 0.736 | 0.706 | 0.646 | 0.596 | 0.556 | 0.561 | 0.562 | 0.515 |
Between to Pooled coverage | 0.405 | 0.4 | 0.398 | 0.474 | 0.379 | 0.476 | 0.548 | 0.573 | ||
case 8 | ~X4/~Y | Between to Pooled consistency | 0.948 | 0.926 | 0.867 | 0.811 | 0.748 | 0.679 | 0.6 | 0.561 |
Between to Pooled coverage | 0.626 | 0.634 | 0.647 | 0.593 | 0.721 | 0.627 | 0.557 | 0.501 | ||
case 9 | X5/Y | Between to Pooled consistency | 0.054 | 0.311 | 0.207 | 0.284 | 0.297 | 0.361 | 0.548 | 0.629 |
Between to Pooled coverage | 0.305 | 0.525 | 0.383 | 0.562 | 0.397 | 0.473 | 0.575 | 0.653 | ||
case 10 | X5/~Y | Between to Pooled consistency | 0.145 | 0.278 | 0.339 | 0.33 | 0.418 | 0.471 | 0.549 | 0.525 |
Between to Pooled coverage | 0.977 | 0.568 | 0.758 | 0.6 | 0.79 | 0.672 | 0.548 | 0.437 | ||
case 11 | ~X5/Y | Between to Pooled consistency | 0.996 | 0.744 | 0.869 | 0.798 | 0.843 | 0.75 | 0.569 | 0.458 |
Between to Pooled coverage | 0.493 | 0.46 | 0.521 | 0.564 | 0.506 | 0.566 | 0.57 | 0.546 | ||
case 12 | ~X5/~Y | Between to Pooled consistency | 0.897 | 0.768 | 0.724 | 0.759 | 0.681 | 0.63 | 0.574 | 0.583 |
Between to Pooled coverage | 0.532 | 0.574 | 0.525 | 0.494 | 0.578 | 0.517 | 0.547 | 0.557 | ||
case 13 | X6/~Y | Between to Pooled consistency | 0.393 | 0.494 | 0.482 | 0.313 | 0.3 | 0.323 | 0.402 | 0.404 |
Between to Pooled coverage | 0.52 | 0.503 | 0.522 | 0.389 | 0.521 | 0.438 | 0.419 | 0.407 | ||
case 14 | ~X6/~Y | Between to Pooled consistency | 0.709 | 0.571 | 0.593 | 0.763 | 0.783 | 0.785 | 0.712 | 0.709 |
Between to Pooled coverage | 0.657 | 0.675 | 0.658 | 0.594 | 0.693 | 0.666 | 0.653 | 0.564 | ||
case 15 | X7/Y | Between to Pooled consistency | 0.649 | 0.671 | 0.625 | 0.645 | 0.547 | 0.548 | 0.591 | 0.544 |
Between to Pooled coverage | 0.493 | 0.479 | 0.501 | 0.512 | 0.474 | 0.517 | 0.517 | 0.611 | ||
case 16 | X7/~Y | Between to Pooled consistency | 0.635 | 0.663 | 0.601 | 0.751 | 0.522 | 0.584 | 0.7 | 0.514 |
Between to Pooled coverage | 0.578 | 0.572 | 0.583 | 0.549 | 0.639 | 0.6 | 0.582 | 0.462 | ||
case 17 | ~X7/~Y | Between to Pooled consistency | 0.444 | 0.395 | 0.485 | 0.332 | 0.571 | 0.53 | 0.419 | 0.567 |
Between to Pooled coverage | 0.602 | 0.592 | 0.61 | 0.462 | 0.641 | 0.56 | 0.494 | 0.499 | ||
Case | Causal Combination | Index | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | |
case 1 | X3/Y | Between to Pooled consistency | 0.551 | 0.654 | 0.618 | 0.659 | 0.667 | 0.688 | 0.699 | |
Between to Pooled coverage | 0.454 | 0.537 | 0.519 | 0.618 | 0.604 | 0.594 | 0.527 | |||
case 2 | X3/~Y | Between to Pooled consistency | 0.746 | 0.812 | 0.673 | 0.69 | 0.712 | 0.686 | 0.691 | |
Between to Pooled coverage | 0.504 | 0.519 | 0.413 | 0.437 | 0.443 | 0.505 | 0.691 | |||
case 3 | ~X3/Y | Between to Pooled consistency | 0.509 | 0.397 | 0.428 | 0.375 | 0.379 | 0.354 | 0.343 | |
Between to Pooled coverage | 0.674 | 0.735 | 0.587 | 0.655 | 0.66 | 0.601 | 0.535 | |||
case 4 | ~X3/~Y | Between to Pooled consistency | 0.316 | 0.255 | 0.378 | 0.362 | 0.356 | 0.37 | 0.352 | |
Between to Pooled coverage | 0.406 | 0.359 | 0.476 | 0.403 | 0.42 | 0.47 | 0.531 | |||
case 5 | X4/Y | Between to Pooled consistency | 0.552 | 0.556 | 0.576 | 0.68 | 0.633 | 0.681 | 0.739 | |
Between to Pooled coverage | 0.522 | 0.543 | 0.481 | 0.633 | 0.564 | 0.541 | 0.506 | |||
case 6 | X4/~Y | Between to Pooled consistency | 0.578 | 0.717 | 0.732 | 0.698 | 0.796 | 0.836 | 0.797 | |
Between to Pooled coverage | 0.53 | 0.53 | 0.562 | 0.415 | 0.481 | 0.497 | 0.528 | |||
case 7 | ~X4/Y | Between to Pooled consistency | 0.502 | 0.518 | 0.476 | 0.372 | 0.418 | 0.367 | 0.311 | |
Between to Pooled coverage | 0.551 | 0.707 | 0.658 | 0.659 | 0.751 | 0.75 | 0.613 | |||
case 8 | ~X4/~Y | Between to Pooled consistency | 0.478 | 0.382 | 0.324 | 0.382 | 0.278 | 0.228 | 0.255 | |
Between to Pooled coverage | 0.509 | 0.394 | 0.412 | 0.432 | 0.34 | 0.349 | 0.486 | |||
case 9 | X5/Y | Between to Pooled consistency | 0.739 | 0.719 | 0.898 | 0.712 | 0.797 | 0.807 | 0.821 | |
Between to Pooled coverage | 0.632 | 0.656 | 0.61 | 0.665 | 0.662 | 0.616 | 0.546 | |||
case 10 | X5/~Y | Between to Pooled consistency | 0.52 | 0.572 | 0.673 | 0.647 | 0.699 | 0.761 | 0.76 | |
Between to Pooled coverage | 0.431 | 0.395 | 0.42 | 0.385 | 0.394 | 0.434 | 0.489 | |||
case 11 | ~X5/Y | Between to Pooled consistency | 0.334 | 0.336 | 0.147 | 0.342 | 0.271 | 0.259 | 0.232 | |
Between to Pooled coverage | 0.418 | 0.51 | 0.328 | 0.603 | 0.571 | 0.591 | 0.5 | |||
case 12 | ~X5/~Y | Between to Pooled consistency | 0.556 | 0.501 | 0.376 | 0.438 | 0.401 | 0.326 | 0.296 | |
Between to Pooled coverage | 0.674 | 0.574 | 0.772 | 0.492 | 0.572 | 0.558 | 0.616 | |||
case 13 | X6/~Y | Between to Pooled consistency | 0.319 | 0.634 | 0.622 | 0.712 | 0.71 | 0.708 | 0.695 | |
Between to Pooled coverage | 0.395 | 0.461 | 0.481 | 0.411 | 0.48 | 0.481 | 0.5 | |||
case 14 | ~X6/~Y | Between to Pooled consistency | 0.752 | 0.462 | 0.43 | 0.392 | 0.422 | 0.381 | 0.373 | |
Between to Pooled coverage | 0.615 | 0.488 | 0.542 | 0.47 | 0.423 | 0.441 | 0.578 | |||
case 15 | X7/Y | Between to Pooled consistency | 0.352 | 0.509 | 0.458 | 0.378 | 0.432 | 0.352 | 0.408 | |
Between to Pooled coverage | 0.493 | 0.617 | 0.511 | 0.52 | 0.649 | 0.759 | 0.671 | |||
case 16 | X7/~Y | Between to Pooled consistency | 0.453 | 0.496 | 0.535 | 0.617 | 0.431 | 0.214 | 0.305 | |
Between to Pooled coverage | 0.616 | 0.455 | 0.548 | 0.541 | 0.439 | 0.345 | 0.485 | |||
case 17 | ~X7/~Y | Between to Pooled consistency | 0.627 | 0.583 | 0.523 | 0.452 | 0.655 | 0.85 | 0.793 | |
Between to Pooled coverage | 0.484 | 0.473 | 0.47 | 0.317 | 0.439 | 0.495 | 0.565 |
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Variable | Index | Indicator Interpretation (Units) | Unit |
---|---|---|---|
Input | Labor | Year-end employment in the forestry sector | People |
Land | Forest area | 103 hm2 | |
Capital | Total amount of investment in fixed assets | 103 CNY | |
Desirable output | Economy | Total value of the primary forestry industry | 103 CNY |
Carbon sink | Forest carbon sink | Ton | |
Undesirable output | Carbon source | Carbon dioxide emissions | Ton |
Variable | Mean | S.D | Min | Max | |
---|---|---|---|---|---|
Input | Year-end employment in the forestry sector | 39,273.35 | 55,711.91 | 496 | 234,181 |
Forest area | 706.89 | 615.11 | 1.89 | 2614.85 | |
Total amount of investment in fixed assets | 600,253.98 | 1,196,293.74 | 518 | 10,861,358 | |
Desirable output | Total value of the primary forestry industry | 19,313,982.53 | 27,734,177.82 | 23,999 | 374,085,252 |
Forest carbon sink | 49,112 | 59,618.34 | 38.51 | 228,537.41 | |
Undesirable output | Carbon dioxide emissions | 153.24 | 110.36 | 5.33 | 777.16 |
Category | Index (Abbreviation) | Indicator Description (Units) | Indicator Direction |
---|---|---|---|
Natural endowment | Forest coverage (FC) Precipitation (PRE) | Ratio of forest area to total land area (%) | + + |
Total annual precipitation (mm) | |||
Social development | Total value of the primary forestry industry (TPFI) | Total forestry primary industry (104 CNY) | + |
Gross regional domestic product (GDP) | 108 CNY | + | |
Year of education (YOE) | Years of education for forestry practitioners (years) | + | |
Forest management | Forestry pest control rate (FPCR) | % | + |
Fire-damaged area (FDA) | Area of forest destroyed by fire (ha) | - |
Variable | Descriptive Statistics | ||||
---|---|---|---|---|---|
Mean | S.D | Min | Max | ||
Result variable | FCSE | 1.10 | 1.78 | 0.02 | 15.12 |
Condition variable | FC | 32.71 | 17.89 | 2.97 | 66.84 |
PRE | 916.76 | 514.64 | 55.90 | 2432.6 | |
TPFI | 6,233,096.32 | 5,369,863.45 | 23284 | 24,215,489 | |
GDP | 24,171.51 | 21,873.23 | 896.9 | 129,118.6 | |
YOE | 14.21 | 0.71 | 10.56 | 15.67 | |
FPCR | 76.66 | 21.87 | 8.45 | 100 | |
FDA | 695.96 | 1799.63 | 0.15 | 17780.99 |
Variable Category | Variable | Full Member | Crossover Point | Full Non-Member |
---|---|---|---|---|
Outcome variable | FCSE | 1.202 | 0.653 | 0.339 |
Antecedent variable | FC | 46.068 | 35.155 | 16.723 |
PRE | 1279.1 | 822.625 | 491.725 | |
TPFI | 9,061,203 | 4,771,543 | 2,018,558 | |
GDP | 31,460.75 | 17,569.2 | 9937.6 | |
YOE | 14.76 | 14.328 | 13.729 | |
FPCR | 93.848 | 82.895 | 66.67 | |
FDA | 578.708 | 133.97 | 29.222 |
Region | FCSE Change Index | ||
---|---|---|---|
ML | EC | TC | |
Beijing | 0.82 | 0.71 | 1.15 |
Tianjin | 0.54 | 0.37 | 1.45 |
Hebei | 1.15 | 1.01 | 1.14 |
Shanxi | 0.94 | 0.59 | 1.59 |
Inner Mongolia | 1.04 | 0.65 | 1.6 |
Liaoning | 0.81 | 0.76 | 1.07 |
Jilin | 1.46 | 1.12 | 1.3 |
Heilongjiang | 1.27 | 0.99 | 1.28 |
Shanghai | 1.29 | 1.1 | 1.17 |
Jiangsu | 1.53 | 1.56 | 0.98 |
Chekiang | 0.38 | 0.4 | 0.94 |
Anhui | 1.08 | 0.79 | 1.37 |
Fujian | 1.08 | 0.97 | 1.12 |
Jiangxi | 1.3 | 1.69 | 0.77 |
Shandong | 0.9 | 0.8 | 1.12 |
Henan | 0.82 | 0.66 | 1.24 |
Hubei | 1.44 | 1.16 | 1.24 |
Hunan | 1.11 | 1.05 | 1.06 |
Guangdong | 1.20 | 1.00 | 1.20 |
Guangxi | 1.18 | 1.00 | 1.18 |
Hainan | 0.74 | 0.76 | 0.98 |
Chongqing | 1.04 | 1.01 | 1.03 |
Sichuan | 1.07 | 0.96 | 1.11 |
Guizhou | 1.34 | 1.26 | 1.06 |
Yunnan | 1.01 | 0.93 | 1.09 |
Shanxi | 1.14 | 1.01 | 1.13 |
Gansu | 0.81 | 0.31 | 2.60 |
Qinghai | 1.03 | 0.97 | 1.06 |
Ningxia | 0.35 | 0.75 | 0.46 |
Xinjiang | 0.52 | 0.45 | 1.16 |
Western remote region | 1.00 | 0.83 | 1.21 |
Central inland region | 1.23 | 0.97 | 1.27 |
Eastern coastal region | 0.99 | 0.88 | 1.12 |
Mean value | 1.09 | 0.90 | 1.21 |
Year | Moran’s I | p | Z |
---|---|---|---|
2008 | −0.07 | 0.27 | −0.61 |
2009 | −0.07 | 0.21 | −0.8 |
2010 | −0.08 | 0.15 | −1.06 |
2011 | −0.08 | 0.2 | −0.85 |
2012 | −0.07 | 0.17 | −0.95 |
2013 | −0.07 | 0.15 | −1.05 |
2014 | −0.07 | 0.12 | −1.16 |
2015 | −0.05 | 0.45 | −0.13 |
2016 | −0.09 | 0.25 | −0.68 |
2017 | 0.03 | 0.23 | 0.75 |
2018 | 0.04 | 0.17 | 0.96 |
2019 | −0.04 | 0.49 | −0.03 |
2020 | 0.05 | 0.25 | 0.68 |
2021 | −0.01 | 0.41 | 0.23 |
2022 | 0.13 | 0.05 | 1.61 |
Variable | High FCSE | |||
---|---|---|---|---|
Pooled Con | Pooled Cov | Between Con Adjusted Distance | Within Con Adjusted Distance | |
FC | 0.56 | 0.584 | 0.085 | 0.679 |
~FC | 0.516 | 0.525 | 0.106 | 0.69 |
PRE | 0.575 | 0.601 | 0.089 | 0.644 |
~PRE | 0.493 | 0.501 | 0.115 | 0.69 |
TPFI | 0.547 | 0.566 | 0.336 | 0.529 |
~TPFI | 0.531 | 0.545 | 0.366 | 0.61 |
GDP | 0.557 | 0.588 | 0.226 | 0.633 |
~GDP | 0.514 | 0.517 | 0.247 | 0.621 |
YOE | 0.566 | 0.589 | 0.524 | 0.5 |
~YOE | 0.508 | 0.518 | 0.558 | 0.575 |
FPCR | 0.616 | 0.621 | 0.119 | 0.529 |
~FPCR | 0.468 | 0.493 | 0.187 | 0.569 |
PDA | 0.507 | 0.541 | 0.23 | 0.575 |
~PDA | 0.572 | 0.569 | 0.192 | 0.581 |
Variable | Configuration Analysis—High | ||
---|---|---|---|
Configuration 1 | Configuration 2 | Configuration 3 | |
Forest coverage | ⮾ | ||
Precipitation | ● | ● | ● |
Total value of the primary forestry industry | ⮾ | ● | |
Gross regional domestic product | ● | ||
Year of education | ● | ||
Forestry pest control rate | ● | ||
Fire-damaged area | ● | ● | |
Consistency | 0.824 | 0.828 | 0.753 |
PRI | 0.755 | 0.768 | 0.534 |
Coverage | 0.175 | 0.179 | 0.091 |
Unique coverage | 0.078 | 0.102 | 0.000 |
Between consistency adjusted distance | 0.123 | 0.162 | 0.115 |
Within consistency adjusted distance | 0.374 | 0.328 | 0.311 |
Overall consistency | 0.822 | ||
Overall PRI | 0.771 | ||
Overall coverage | 0.318 |
Region | Configuration 1 | Configuration 2 | Configuration 3 |
---|---|---|---|
Eastern coastal region | 0.31 | 0.272 | 0.164 |
Central inland region | 0.327 | 0.203 | 0.363 |
Western remote region | 0.117 | 0.249 | 0.198 |
Variable | The Frequency Is 4 | The Frequency Is 5 | |||
---|---|---|---|---|---|
Configuration 1 | Configuration 2 | Configuration 3 | Configuration 1 | Configuration 2 | |
FC | ⊗ | ||||
PRE | ● | ● | ● | ● | ● |
TPFI | ⊗ | ● | ⊗ | ||
GDP | ● | ● | |||
YOE | ● | ● | |||
FPCR | ● | ● | |||
FDA | ● | ● | ● | ||
Consistency | 0.824 | 0.828 | 0.753 | 0.824 | 0.828 |
PRI | 0.755 | 0.768 | 0.534 | 0.755 | 0.768 |
Coverage | 0.175 | 0.179 | 0.091 | 0.175 | 0.179 |
Unique coverage | 0.078 | 0.102 | 0 | 0.038 | 0.126 |
Between consistency adjusted distance | 0.822 | 0.827 | |||
Within consistency adjusted distance | 0.771 | 0.776 | |||
Overall consistency | 0.318 | 0.3 |
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Share and Cite
Ding, Y.; Zhao, J.; Li, C. Dynamic Evaluation of Forest Carbon Sink Efficiency and Its Driver Configurational Identification in China: A Sustainable Forestry Perspective. Sustainability 2025, 17, 5931. https://doi.org/10.3390/su17135931
Ding Y, Zhao J, Li C. Dynamic Evaluation of Forest Carbon Sink Efficiency and Its Driver Configurational Identification in China: A Sustainable Forestry Perspective. Sustainability. 2025; 17(13):5931. https://doi.org/10.3390/su17135931
Chicago/Turabian StyleDing, Yingyiwen, Jing Zhao, and Chunhua Li. 2025. "Dynamic Evaluation of Forest Carbon Sink Efficiency and Its Driver Configurational Identification in China: A Sustainable Forestry Perspective" Sustainability 17, no. 13: 5931. https://doi.org/10.3390/su17135931
APA StyleDing, Y., Zhao, J., & Li, C. (2025). Dynamic Evaluation of Forest Carbon Sink Efficiency and Its Driver Configurational Identification in China: A Sustainable Forestry Perspective. Sustainability, 17(13), 5931. https://doi.org/10.3390/su17135931