Measurement, Spatiotemporal Evolution, and Spatial Spillover Effects of Carbon Sinks and Emissions from Shellfish and Algae Mariculture in China
Abstract
1. Introduction
2. Methods
2.1. Study Area
2.2. Data Sources
- (1)
- Data on shellfish and algae production in mariculture, seaweed culture area, the power of mariculture fishing vessels, and pond and factory production in mariculture were obtained from the China Fisheries Statistical Yearbook (CFSY, 2008–2024).
- (2)
- Shellfish and algae biomass carbon sink correlation coefficients, including dry-to-wet coefficients for shellfish and algae, the specific gravity of shell and soft tissue in shellfish, the corresponding carbon content, and algal carbon content, were mainly derived from the research results of Sun et al. [30].
- (3)
- The ratio of carbon entering biological deposition to carbon removed by harvesting during shellfish aquaculture was obtained from Tang and Liu [31]. Conversion coefficients between net primary productivity (NPP) and gross primary productivity (GPP) for carbon sink accounting of RDOC carbon sinks in algae were obtained from Zhang et al. [32]. Conversion coefficients between GPP and DOC were obtained from Abdullah and Fredriksen [33] and Krause-Jensen and Duarte [34]. Conversion coefficients between DOC and RDOC [22] were obtained from previous studies. The average deposition rates for algae POC carbon sink accounting for seaweed farming areas were obtained from Zhang et al. [22] and Cai et al. [35]. The percentage of sedimentary organic carbon content in seaweed farming areas [36] was obtained from previous research results.
- (4)
- The conversion coefficient for diesel consumption by mariculture fishing vessels was obtained from the Reference Standard for Measuring Oil Consumption for Domestic Motorized Fishing Vessel Oil Price Subsidy of the Ministry of Agriculture and Rural Affairs. The carbon emission coefficient of diesel fuel was obtained from the General Rules for Calculating Comprehensive Energy Consumption (GB/T2589-2008) and the Guidelines for the Preparation of Provincial Greenhouse Gas Inventories (using raw coal as an example). The conversion coefficient of electric power consumption generated in the process of pond and factory aquaculture was obtained from Xu [28], and the carbon emission coefficient of electricity was obtained from the 2021 Electricity CO2 Emission Factors.
2.3. Method
2.3.1. Net Carbon Value
2.3.2. Total Carbon Emissions
2.3.3. Total Carbon Sinks
Shellfish Carbon Sinks
- (1)
- Shellfish biomass carbon sinks:
- (2)
- Shellfish POC carbon sinks:
Algal Carbon Sinks
- (1)
- Algal biomass carbon sinks:
- (2)
- Algal RDOC carbon sinks:
- (3)
- Algal POC carbon sinks:
2.3.4. Geographically Weighted Regression (GWR)
2.3.5. Spatial Durbin Model (SDM)
3. Results and Discussion
3.1. Spatial–Temporal Evolution Characterization of the National Perspective
3.2. Spatial–Temporal Evolution Characterization at the Provincial Scale
3.2.1. Spatiotemporal Evolution Analysis of Carbon Emissions from China’s Mariculture
3.2.2. Spatiotemporal Evolution Analysis of Carbon Sinks in China’s Mariculture
3.2.3. Spatiotemporal Evolution Analysis of Net Carbon Values in China’s Mariculture
4. Analysis of Influencing Factors on Carbon Emissions and Carbon Sinks from Mariculture in China
4.1. Variable Selection
4.2. Spatial Heterogeneity of Factors Influencing Carbon Emissions and Sinks
4.2.1. OLS Model and Results
4.2.2. GWR Model and Results
4.2.3. Analysis and Discussion of Spatial Heterogeneity of Factors Influencing Carbon Emissions and Sinks
4.3. Spatial Spillover Effects of Factors Influencing Carbon Emissions and Sinks
4.3.1. Autocorrelation Test Results
4.3.2. Model Applicability Tests
4.3.3. Analysis and Discussion of Spatial Spillover Effects of Factors Affecting Carbon Emissions and Sinks
5. Policy Recommendations
6. Conclusions and Prospects
6.1. Conclusions
6.2. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Year | Liaoning | Hebei | Shandong | Jiangsu | Zhejiang | Fujian | Guangdong | Guangxi | Hainan | Total Area (ha) |
---|---|---|---|---|---|---|---|---|---|---|
2008 | 411,556 | 109,755 | 426,217 | 160,089 | 96,139 | 120,704 | 189,717 | 47,380 | 12,983 | 1,574,540 |
2009 | 630,700 | 121,013 | 441,403 | 172,754 | 94,514 | 133,942 | 194,766 | 50,670 | 15,247 | 1,855,009 |
2010 | 763,101 | 123,810 | 500,946 | 192,426 | 93,905 | 137,636 | 199,258 | 51,287 | 14,529 | 2,076,898 |
2011 | 751,387 | 134,264 | 512,126 | 201,073 | 90,839 | 142,315 | 203,410 | 52,212 | 14,646 | 2,102,272 |
2012 | 813,035 | 134,682 | 523,705 | 199,352 | 89,747 | 145,486 | 201,834 | 53,249 | 15,845 | 2,176,935 |
2013 | 942,050 | 117,928 | 546,814 | 193,807 | 89,358 | 154,453 | 197,198 | 54,001 | 16,791 | 2,312,400 |
2014 | 928,503 | 122,434 | 548,487 | 188,657 | 88,178 | 161,418 | 193,691 | 54,233 | 16,691 | 2,302,292 |
2015 | 933,068 | 117,533 | 563,198 | 181,829 | 85,881 | 166,075 | 194,861 | 55,015 | 17,138 | 2,314,598 |
2016 | 698,400 | 124,800 | 604,800 | 185,480 | 78,701 | 153,000 | 166,200 | 45,400 | 32,323 | 2,089,104 |
2017 | 698,400 | 107,583 | 610,377 | 192,390 | 75,954 | 155,739 | 161,690 | 47,022 | 31,715 | 2,080,870 |
2018 | 693,190 | 111,404 | 570,857 | 186,641 | 80,924 | 162,464 | 165,614 | 47,844 | 21,372 | 2,040,310 |
2019 | 661,817 | 107,041 | 561,501 | 179,951 | 82,019 | 163,713 | 164,990 | 49,822 | 20,510 | 1,991,364 |
2020 | 650,719 | 105,341 | 580,350 | 177,629 | 82,535 | 163,144 | 164,719 | 52,277 | 17,595 | 1,994,309 |
2021 | 647,606 | 104,185 | 608,376 | 171,106 | 81,466 | 164,641 | 166,805 | 64,255 | 15,881 | 2,024,321 |
2022 | 677,201 | 105,587 | 617,464 | 172,188 | 83,439 | 167,953 | 166,596 | 67,393 | 15,594 | 2,073,415 |
2023 | 773,974 | 105,022 | 646,026 | 171,452 | 84,255 | 177,321 | 172,133 | 69,075 | 14,673 | 2,213,931 |
Parameters | Coefficient | T Value | p Value | Standard Deviation | VIF |
---|---|---|---|---|---|
Energy intensity | 0.790 | 9.091 | 0.000 * | 0.087 | 1.053 |
Upgrading of industrial structure | −0.150 | −2.399 | 0.000 * | 0.063 | 1.053 |
R2 | 0.421 | ||||
Adjusted R2 | 0.411 | ||||
Join F(P) | 0.000 * | ||||
Jarque-Bera Test | 8.916 | ||||
AICc | 253.188 |
Parameters | Coefficient | T Value | p Value | Standard Deviation | VIF |
---|---|---|---|---|---|
Technical level | 2.157 | 6.139 | 0.000 * | 0.351 | 1.370 |
Industrial structure | 0.597 | 0.559 | 0.480 | 1.068 | 2.899 |
Energy intensity | 1.402 | 8.204 | 0.000 * | 0.171 | 3.395 |
Level of ecological resource constraints | 2.426 | 4.344 | 0.002 * | 0.559 | 1.150 |
R2 | 0.691 | ||||
Adjusted R2 | 0.680 | ||||
Join F(P) | 0.000 * | ||||
Jarque-Bera Test | 2.216 | ||||
AICc | 276.735 |
Parameters | Bandwidth | Residual Squares | Sigma | AICc | R2 | Adjusted R2 |
---|---|---|---|---|---|---|
Value | 0.115 | 2.242 | 0.138 | −92.491 | 0.977 | 0.976 |
Parameters | Bandwidth | Residual Squares | Sigma | AICc | R2 | Adjusted R2 |
---|---|---|---|---|---|---|
Value | 0.433 | 47.058 | 0.634 | 242.818 | 0.780 | 0.774 |
Test Item | Test Value | p-Value |
---|---|---|
LM (SAR) | 96.484 | 0.000 |
Robust LM (SAR) | 19.519 | 0.000 |
LM (SEM) | 79.725 | 0.000 |
Robust LM (SEM) | 2.760 | 0.097 |
Hausman (Fixed Effect) | 8.23 | 0.016 |
Wald (SAR) | 29.25 | 0.000 |
Wald (SEM) | 8.17 | 0.017 |
LR (SAR) | 27.01 | 0.000 |
LR (SEM) | 21.98 | 0.000 |
Test Item | Test Value | p-Value |
---|---|---|
LM (SAR) | 70.222 | 0.000 |
Robust LM (SAR) | 5.153 | 0.000 |
LM (SEM) | 82.310 | 0.000 |
Robust LM (SEM) | 17.241 | 0.097 |
Hausman (Fixed Effect) | 123.26 | 0.016 |
Wald (SAR) | 25.87 | 0.000 |
Wald (SEM) | 27.53 | 0.017 |
LR (SAR) | 61.29 | 0.000 |
LR (SEM) | 97.39 | 0.000 |
Variables | Local Effect | Neighborhood Effect | Total Effect |
---|---|---|---|
Energy intensity | 0.671 *** | 0.581 *** | 1.252 *** |
(6.95) | (2.84) | (7.03) | |
Upgrading of industrial structure | −0.280 *** (−4.22) | −0.460 *** (−4.92) | −0.740 *** (−5.95) |
rho | −0.551 *** | ||
(−3.81) | |||
sigma2_e | 0.339 *** | ||
(−7.31) | |||
Observations | 117 | ||
R-squared | 0.403 |
Variables | Local Effect | Neighborhood Effect | Total Effect |
---|---|---|---|
Technical level | 7.834 *** | −4.893 *** | 2.941 |
(7.38) | (−2.93) | (1.36) | |
Energy intensity | 1.432 *** | 1.203 *** | 2.635 *** |
(16.09) | (5.66) | (11.75) | |
Level of ecological resource constraints | 3.399 *** (8.86) | 0.209 (0.27) | 3.608 *** (3.91) |
rho | −0.484 *** | ||
(−3.54) | |||
sigma2_e | 0.195 *** | ||
(−8.07) | |||
Observations | 117 | ||
R-squared | 0.781 |
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Zeng, H.; Wu, X.; Chen, X.; Wang, H. Measurement, Spatiotemporal Evolution, and Spatial Spillover Effects of Carbon Sinks and Emissions from Shellfish and Algae Mariculture in China. Fishes 2025, 10, 301. https://doi.org/10.3390/fishes10070301
Zeng H, Wu X, Chen X, Wang H. Measurement, Spatiotemporal Evolution, and Spatial Spillover Effects of Carbon Sinks and Emissions from Shellfish and Algae Mariculture in China. Fishes. 2025; 10(7):301. https://doi.org/10.3390/fishes10070301
Chicago/Turabian StyleZeng, Han, Xuexue Wu, Xiaoyu Chen, and Haohan Wang. 2025. "Measurement, Spatiotemporal Evolution, and Spatial Spillover Effects of Carbon Sinks and Emissions from Shellfish and Algae Mariculture in China" Fishes 10, no. 7: 301. https://doi.org/10.3390/fishes10070301
APA StyleZeng, H., Wu, X., Chen, X., & Wang, H. (2025). Measurement, Spatiotemporal Evolution, and Spatial Spillover Effects of Carbon Sinks and Emissions from Shellfish and Algae Mariculture in China. Fishes, 10(7), 301. https://doi.org/10.3390/fishes10070301