Multifactor Configurational Pathways Driving the Eco-Efficiency of Cultivated Land Utilization in China: A Dynamic Panel QCA
Abstract
1. Introduction
2. Theoretical Framework
3. Research Design
3.1. Research Methodology
3.1.1. Super-SBM Model with Undesirable Outputs
3.1.2. Dynamic QCA
3.1.3. Necessary Condition Analysis
3.2. Index Selection
3.2.1. Outcome Variable
3.2.2. Conditional Variable
3.3. Data Calibration
3.4. Study Area
3.5. Data Source
4. Results
4.1. Necessity Analysis of Single Conditions
4.1.1. Necessity Analysis
4.1.2. Necessity Analysis of a Single Condition in QCA
4.2. Configuration Analysis
4.3. Pooled Results Analysis
- (1)
- Economy–Technology–Government Synergistic Pathway. The configuration H1a demonstrates a consistency of 0.925 and a coverage of 0.391, indicating that 39.1% of the cases can be accounted for by this configuration. Its core conditions are a high GPC, high STI, low AMD, and high GAEI, with a high UL serving as a peripheral condition. This combination is sufficient to attain a high ECLU. The configuration H1b explains 24% of the cases, as it achieves a consistency of 0.942 and a coverage of 0.240. Although it shares the same core conditions—high GPC, high STI, low AMD, and high GAEI—it relies on a high MCI and high ACL as peripheral conditions to achieve a high ECLU. Thus, both configurations have identical cores, while their peripheral conditions are substitutable. Financial and technological resources can compensate for lower mechanization density or limited natural endowments in provinces with a strong economic capacity, active agricultural innovation, and robust policy support. This enables precise and efficient resource allocation and, ultimately, a high ECLU. Regions with higher levels of economic development typically command greater advantages in the allocation of agricultural inputs, enabling them to raise the ECLU [72]. Shanghai illustrates this dynamic. As China’s economic center, Shanghai leverages its robust economic strength to keep upgrading agricultural production conditions and thus consistently raise ECLU. In 2021, Shanghai’s contribution rate of agricultural scientific and technological progress reached 79.09%, among the highest nationwide, and the city consistently ranks near the top in agricultural modernization and indigenous innovation capacity [73]. Policy support reinforces these strengths. In 2025, the municipal government issued the Implementation Opinions on Accelerating Agricultural Science-and-Technology Innovation, calling for an integrated innovation system that links universities, research institutes, and enterprises to boost in-house R&D and speed up the commercialization of research outputs. A 2024 notice—On the Allocation of Funds for Cultivated Land Fertility Protection Subsidies—directs government subsidies to soil fertility protection, promoting pollution abatement and carbon mitigation in cultivated land use and further enhancing the city’s ECLU.
- (2)
- Nature–Economy Dual-Driver Pathway. Configuration H2 obtains a consistency of 0.911 and a coverage of 0.299, indicating that 29.9% of the cases can be accounted for by this configuration. Its core conditions are a high ACL, high GPC, low MCI, and low AMD; a high UL and low STI serve as peripheral conditions. In practice, where natural constraints keep both re-cropping intensity and mechanization modest, expanding the scope of cultivated land operations, combined with strong economic resources, can offset limited technological input and still deliver a high ECLU. Inner Mongolia exemplifies this pathway. Owing to its climate, the region produces only one crop per year, yielding a low MCI. Yet, in 2023, its cultivated land endowment reached 11,466.7 kha—about 10% of China’s total and second only to Heilongjiang. In 2024, GPC stood at CNY 110,011, eighth nationwide and among the highest in China’s central–western region. Prior work contends that favorable economic conditions reinforce farmers’ ecological awareness and encourage conservation tillage, allowing land abundance to translate into a high ECLU despite technological limitations [74].
- (3)
- Government-Supported Land–Economy Pathway. Configuration H3a records a consistency of 0.926 and a coverage of 0.281, indicating that 28.1% of the cases can be accounted for by this configuration. Its core conditions are a high ACL, high GPC, high GAEI, and low MCI and STI; a high UL serves as a peripheral condition. Together, these features are sufficient to achieve a high ECLU. Configuration H3b achieves a consistency of 0.940 and a coverage of 0.267, accounting for 26.7% of the cases. It shares the same core conditions as H3a—high ACL, high GPC, high GAEI, and sub-threshold MCI and STI—and adds a low AMD. Thus, the two configurations possess identical cores and substitute each other in their peripheral requirements. Xinjiang exemplifies this pathway. The region’s contribution rate of agricultural scientific and technological progress has long lagged behind the national average [75], and its harsh inland climate limits multiple cropping, yielding a low MCI. Yet, Xinjiang commands vast cultivated land—about 7066.7 kha, or 5.5% of China’s total—and strong fiscal support: in 2024, budgetary expenditure on agriculture, forestry, and water affairs reached CNY 98.63 billion, up 22.6% year-on-year. By leveraging extensive land resources, a robust economic capacity, and vigorous government funding, Xinjiang compensates for technological and natural constraints and achieves a higher ECLU.
4.4. Between Results
4.5. Within Results
4.6. Configurational Analysis of Low ECLU
4.7. Further Analysis
4.8. Robustness Test
5. Discussion
Limitations and Future Work
6. Conclusions
6.1. Theoretical Implications
6.2. Practical Implications
- (1)
- For provinces aligning with the Economy–Technology–Government Synergistic Pathway, governments should effectively allocate agricultural fiscal resources by providing subsidies to farmers adopting efficient and environmentally friendly production inputs and offering targeted incentives for precision agriculture and other advanced technologies. Resources should also be strategically channeled into research and development for ecological agricultural technologies. Additionally, fiscal subsidies could prioritize reducing chemical fertilizers, minimizing pesticide use, and enhancing soil remediation. Financial instruments, such as green agricultural bonds, may further accelerate the adoption of energy-efficient agricultural machinery. In regions characterized by fragmented farmland ownership, governments could facilitate land transfers to enable large-scale cultivation, with cooperatives or professional agricultural service providers delivering technical support and mechanized services.
- (2)
- For provinces characterized by the Nature–Economy Dual-Driver Pathway, governments should enhance fiscal investments in agriculture, particularly in farmland infrastructural improvements such as irrigation facilities, aiming to increase multiple-cropping indices and mechanization levels. Collaboration with local research institutes, universities, and technical colleges is also essential to deliver targeted farmer training, facilitate the dissemination of agricultural scientific knowledge, and provide tailored technological support. Moreover, the promotion of regionally appropriate green cultivation practices and strengthened investment in digital agricultural infrastructure should be prioritized to elevate local agricultural technological capacities.
- (3)
- In provinces explained by the Government-Supported Land–Economy Pathway, governments should increase technical subsidies targeted towards regions lagging in agricultural technology. Research institutes and universities should be encouraged to develop new crop varieties and technologies specifically suited to areas with low MCI, such as drought-resistant, high-yield cultivars and conservation tillage machinery. Fiscal subsidies can then accelerate adoption, phasing out outdated, energy-intensive equipment, thereby enhancing the mechanization density while reducing resource consumption and emissions.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
References
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Name | Variables | Description | Unit |
---|---|---|---|
Input | Land | Sown area of crops | 103 ha |
Labor force | Agricultural workers | 104 People | |
Irrigation | Effective irrigation area | 103 ha | |
Agricultural machinery | Total power of agricultural machinery | 104 kW | |
Agricultural plastic film | Consumption of agricultural plastic film | 104 T | |
Pesticide | Consumption of pesticides | 104 T | |
Fertilizers | Consumption of chemical fertilizers | 104 T | |
Desirable output | Carbon sink | Total agricultural carbon sequestration | 104 T |
Economic output | Gross agricultural production | 108 CNY | |
Social output | Output of grain | 104 T | |
Undesirable output | Carbon emissions | Total agricultural carbon emissions | 104 T |
Non-point-source pollution | Total agricultural non-point-source pollution | 109 m3 |
Crops |
Economic Coefficients | Moisture Contents (%) | Carbon Absorption |
---|---|---|---|
Rice | 0.45 | 12 | 0.414 |
Wheat | 0.40 | 12 | 0.485 |
Corn | 0.40 | 13 | 0.471 |
Millet | 0.42 | 12 | 0.450 |
Sorghum | 0.35 | 12 | 0.450 |
Beans | 0.34 | 13 | 0.450 |
Rapeseed | 0.25 | 10 | 0.450 |
Peanuts | 0.43 | 10 | 0.450 |
Sunflower seed | 0.30 | 10 | 0.450 |
Cotton | 0.10 | 8 | 0.450 |
Tubers | 0.70 | 70 | 0.423 |
Sugarcane | 0.50 | 50 | 0.450 |
Sugar beet | 0.70 | 75 | 0.407 |
Vegetables (including cucurbits) | 0.60 | 90 | 0.450 |
Tobacco leaf | 0.55 | 85 | 0.450 |
Carbon Source | Carbon Emission Coefficient | Reference Source |
---|---|---|
Fertilizer | 0.8956 kg/kg | West et al. [54] |
Pesticide | 4.9341 kg/kg | Liu et al. [55] |
Agricultural plastic film | 5.18 kg/kg | Agricultural Resources and Ecological Environment Institute, Nanjing Agricultural University |
Diesel | 0.5927 kg/kg | I.P.C.C. Climate Change The Fourth Assessment Report of the Intergovernmental Panel on Climate Change [56] |
Tillage | 312.6 kg/km2 | College of Biotechnology, China Agricultural University |
Agricultural irrigation | 20.476 kg/ha | Li et al. [57] |
Dimension | Variable | Description | Reference Source |
---|---|---|---|
Natural conditions dimension | Multiple Cropping Index | Sown area of crops/total cultivated area (%) | Li et al. [36] |
Area of Cultivated Land | Total cultivated area | Yang et al. [29] | |
Socioeconomic dimension | GDP per Capita | GDP per capita at constant 2005 prices | Kuang et al. [28] |
Urbanization Level | Urban population/total population (%) | Fan et al. [15] | |
Agricultural technology dimension | Investment in Science and Technology | Local expenditures on science and technology/general budgetary expenditures of local governments | Li et al. [36] |
Agricultural Mechanization Density | Total power of agricultural machinery/sown area of crops | Ma et al. [30] | |
Government support dimension | Government Agricultural Expenditure Intensity | Budgetary expenditure on agriculture, forestry, and water affairs/sown area of crops | Lyu et al. [31] |
Calibration | Descriptive Statistics | |||||||
---|---|---|---|---|---|---|---|---|
Variable Category | Variable | Full Membership | Crossover Point | Full Non-Membership | Mean | Standard Deviation | Min. | Max. |
Outcome variable | ECLU | 1.01764367 | 0.56692627 | 0.362773932 | 0.6326724 | 0.2218311 | 0.3010296 | 1.062032 |
Conditional variable | MCI | 211.241622 | 133.010315 | 75.6259658 | 133.022 | 44.76552 | 53.07357 | 253.5735 |
ACL | 9195.156 | 4064.18 | 223.01 | 4152.403 | 3263.794 | 93.5 | 17,195.4 | |
GPC | 86,595.0721 | 33,109.3577 | 11,025.43094 | 38,714.9 | 24,694.51 | 5184.857 | 142,784.9 | |
UL | 86.26 | 55.49 | 33.692 | 55.95467 | 14.67222 | 20.85 | 89.6 | |
STI | 5.40954175 | 1.33189921 | 0.676944618 | 2.017749 | 1.463377 | 0.3029005 | 7.201887 | |
AMD | 12.0744796 | 5.59093644 | 2.995104944 | 6.480629 | 3.386691 | 2.105531 | 24.62582 | |
GAEI | 74,125.7207 | 9425.1557 | 1169.77069 | 22,496.5 | 58,139.15 | 460.6958 | 659,842 |
Variable | Method | Ceiling Zone | Effect Size (d) | C-Accuracy (%) | p-Value |
---|---|---|---|---|---|
MCI | CE | 0 | 0 | 100% | 0.962 |
CR | 0 | 0 | 99.50% | 0.953 | |
ACL | CE | 0 | 0 | 100% | 0.95 |
CR | 0 | 0 | 99.30% | 0.969 | |
GPC | CE | 0.002 | 0.002 | 100% | 0.868 |
CR | 0.002 | 0.002 | 99.70% | 0.868 | |
UL | CE | 0.002 | 0.002 | 100% | 0.786 |
CR | 0.02 | 0.022 | 95.40% | 0.474 | |
STI | CE | 0 | 0 | 100% | 0.979 |
CR | 0.003 | 0.003 | 99.00% | 0.836 | |
AMD | CE | 0.004 | 0.005 | 100% | 0.445 |
CR | 0.003 | 0.003 | 99.80% | 0.82 | |
GAEI | CE | 0.008 | 0.008 | 100% | 0 |
CR | 0.072 | 0.08 | 85.20% | 0.05 |
ECLU | MCI | ACL | GPC | UL | STI | AMD | GAEI |
---|---|---|---|---|---|---|---|
0 | NN | NN | NN | NN | NN | NN | NN |
10 | NN | NN | NN | NN | NN | NN | NN |
20 | NN | NN | NN | NN | NN | NN | NN |
30 | NN | NN | NN | NN | NN | NN | NN |
40 | NN | NN | NN | NN | NN | NN | NN |
50 | NN | NN | NN | NN | NN | NN | NN |
60 | NN | NN | NN | NN | 0 | NN | NN |
70 | NN | NN | NN | NN | 0.4 | NN | NN |
80 | NN | NN | NN | NN | 0.8 | NN | 14.4 |
90 | NN | NN | NN | NN | 1.2 | NN | 37.9 |
100 | 17.6 | 1.7 | 67.9 | 63.7 | 1.5 | 73.9 | 61.5 |
Conditional Variable | High ECLU | Low ECLU | ||||||
---|---|---|---|---|---|---|---|---|
POCONS | POCOV | BECONS Adjusted Distance | WICONS Adjusted Distance | POCONS | POCOV | BECONS Adjusted Distance | WICONS Adjusted Distance | |
MCI | 0.595 | 0.637 | 0.061121 | 0.577812 | 0.579 | 0.642 | 0.249186 | 0.560302 |
∼MCI | 0.666 | 0.604 | 0.178662 | 0.449409 | 0.673 | 0.632 | 0.150452 | 0.4319 |
ACL | 0.59 | 0.628 | 0.14575 | 0.583648 | 0.6 | 0.662 | 0.23038 | 0.560302 |
∼ACL | 0.683 | 0.623 | 0.065823 | 0.4319 | 0.663 | 0.626 | 0.183363 | 0.466919 |
GPC | 0.708 | 0.75 | 0.357323 | 0.321006 | 0.504 | 0.553 | 0.498372 | 0.461082 |
∼GPC | 0.578 | 0.529 | 0.371428 | 0.402717 | 0.772 | 0.732 | 0.112839 | 0.31517 |
UL | 0.721 | 0.746 | 0.23038 | 0.326843 | 0.535 | 0.573 | 0.40434 | 0.496101 |
∼UL | 0.588 | 0.549 | 0.286799 | 0.396881 | 0.763 | 0.739 | 0.089331 | 0.350189 |
STI | 0.641 | 0.658 | 0.112839 | 0.437736 | 0.593 | 0.631 | 0.23038 | 0.466919 |
∼STI | 0.641 | 0.603 | 0.17396 | 0.420227 | 0.679 | 0.662 | 0.075226 | 0.41439 |
AMD | 0.58 | 0.599 | 0.225678 | 0.478591 | 0.651 | 0.696 | 0.263291 | 0.379371 |
∼AMD | 0.705 | 0.661 | 0.211573 | 0.391044 | 0.625 | 0.606 | 0.206871 | 0.478591 |
GAEI | 0.649 | 0.758 | 0.34792 | 0.303497 | 0.507 | 0.613 | 0.564195 | 0.396881 |
∼GAEI | 0.669 | 0.567 | 0.253888 | 0.31517 | 0.8 | 0.703 | 0.155154 | 0.297661 |
Conditional Variable | High ECLU | Low ECLU | ||||||
---|---|---|---|---|---|---|---|---|
H1a | H1b | H2 | H3a | H3b | H4 | H5 | H6 | |
MCI | ||||||||
ACL | ||||||||
GPC | ||||||||
UL | ||||||||
STI | ||||||||
AMD | ||||||||
GAEI | ||||||||
Consistency | 0.925 | 0.942 | 0.911 | 0.926 | 0.940 | 0.885 | 0.898 | 0.865 |
PRI | 0.807 | 0.693 | 0.751 | 0.744 | 0.785 | 0.690 | 0.740 | 0.671 |
Coverage | 0.391 | 0.24 | 0.299 | 0.281 | 0.267 | 0.293 | 0.299 | 0.314 |
Unique coverage | 0.113 | 0.001 | 0.039 | 0.021 | 0.006 | 0.006 | 0.081 | 0.012 |
BECONS adjusted distance | 0.0799276 | 0.05171786 | 0.0893308 | 0.075226 | 0.0517179 | 0.155153574 | 0.169258445 | 0.169258445 |
WICONS adjusted distance | 0.1050567 | 0.08754722 | 0.1108931 | 0.1050567 | 0.0933837 | 0.180930926 | 0.157585 | 0.233459259 |
Overall consistency | 0.901 | 0.863 | ||||||
Overall PRI | 0.782 | 0.711 | ||||||
Overall coverage | 0.507 | 0.413 |
Regional | Economy–Technology–Government Synergistic Pathway | Nature–Economy Dual-Driver Pathway | Government-Supported Land–Economy Pathway | ||
---|---|---|---|---|---|
H1a | H1b | H2 | H3a | H3b | |
Eastern China | 0.455 | 0.241 | 0.234 | 0.293 | 0.220 |
Central China | 0.508 | 0.408 | 0.373 | 0.315 | 0.312 |
Western China | 0.319 | 0.238 | 0.366 | 0.356 | 0.369 |
Conditional Variable | Test | ||||
---|---|---|---|---|---|
J1 | J2 | J3 | J4 | J5 | |
MCI | ⊗ | ⊗ | ⊗ | ● | |
ACL | ● | ● | ● | ● | |
GPC | ● | ● | ● | ● | ● |
UL | ● | ● | ● | ||
STI | ● | ⊗ | ⊗ | ⊗ | ● |
AMD | ⊗ | ⊗ | ⊗ | ⊗ | |
GAEI | ● | ● | ● | ● | |
Consistency | 0.925 | 0.911 | 0.926 | 0.94 | 0.942 |
PRI | 0.807 | 0.751 | 0.744 | 0.785 | 0.693 |
Coverage | 0.391 | 0.299 | 0.281 | 0.267 | 0.24 |
Unique coverage | 0.113 | 0.039 | 0.021 | 0.006 | 0.001 |
Overall consistency | 0.901 | ||||
Overall PRI | 0.782 | ||||
Overall coverage | 0.507 |
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Xu, Z.; Duan, J.; Zhan, L.; Yan, C.; Huang, Z. Multifactor Configurational Pathways Driving the Eco-Efficiency of Cultivated Land Utilization in China: A Dynamic Panel QCA. Land 2025, 14, 1549. https://doi.org/10.3390/land14081549
Xu Z, Duan J, Zhan L, Yan C, Huang Z. Multifactor Configurational Pathways Driving the Eco-Efficiency of Cultivated Land Utilization in China: A Dynamic Panel QCA. Land. 2025; 14(8):1549. https://doi.org/10.3390/land14081549
Chicago/Turabian StyleXu, Zihao, Jialong Duan, Lei Zhan, Chuanmin Yan, and Zhigang Huang. 2025. "Multifactor Configurational Pathways Driving the Eco-Efficiency of Cultivated Land Utilization in China: A Dynamic Panel QCA" Land 14, no. 8: 1549. https://doi.org/10.3390/land14081549
APA StyleXu, Z., Duan, J., Zhan, L., Yan, C., & Huang, Z. (2025). Multifactor Configurational Pathways Driving the Eco-Efficiency of Cultivated Land Utilization in China: A Dynamic Panel QCA. Land, 14(8), 1549. https://doi.org/10.3390/land14081549