The Spatial Disequilibrium and Dynamic Evolution of the Net Agriculture Carbon Effect in China

Abstract

Considering the comparative perspective of the net agricultural carbon effect in China’s three major functional grain production areas, the Dagum Gini coefficient, kernel density estimation and Markov chain analysis are used to investigate the spatial disequilibrium and dynamic evolution characteristics of the net agricultural carbon effect in China from 2000 to 2019. The results show that the overall net agricultural carbon sink in China is on a fluctuating upward trend, and the net agricultural carbon sink in the main production areas is higher than that in main marketing areas and balanced production and marketing areas. There are obvious differences in the net agricultural carbon sink between different areas, and the differences are expanding; inter-regional differences are the most significant, with the contribution of intra-regional differences second and the contribution of intensity of transvariation the least. The kernel density curve shows that the absolute differences are increasing and that there are gradients and multipolar differentiation within the area. The Markov transfer matrix reflects that the net agricultural carbon effect in China is highly volatile and has a strong internal mobility. The probability of upward shift in an area increases when it is adjacent to a high-level area, and the net carbon effect of agriculture in high-level areas has a strong stability. Based on this, each area should build on its own comparative advantages and explore targeted pathways to reducing emissions and increasing sinks in agriculture while strengthening inter-regional communication and cooperation. It is necessary to build a synergistic mechanism to enhance the net carbon effect of agriculture, which will ultimately help to achieve the “double carbon” target.

Distribution Characteristics of Drought Resistance and Disaster Reduction Capability and the Identification of Key Factors—A Case Study of a Typical Area in the Yun–Gui Plateau, China

“…Using spatial analysis tools, the transition process between different levels of drought resistance capacity as reflected in the research units was quantitatively calculated in a transition matrix, as shown in Table 5 (where the rows in the transition matrix represent drought resistance capacity levels based on the fuzzy evaluation method, and the columns represent levels based on the supply-demand balance method). The elements on the diagonal of the state transition probability matrix represent the probability of transition between the same levels using both methods, while the off-diagonal elements represent the probability of transitioning between different levels [47,48]. Table 5 shows that in the low-level transitions, the probability values on the diagonal are greater than the off-diagonal probability values, indicating some stability between these levels.…”

Spatial Distribution Pattern, Evolution and Influencing Mechanism of Ecological Farms in China

“…where h denotes the search radius, (x − x i ) 2 + (y − y i ) 2 denotes the distance from the estimated point X to the ith point and n is the total number of ecological farms [51].…”

Research on the Spatial-Temporal Patterns of Carbon Effects and Carbon-Emission Reduction Strategies for Farmland in China

“…To achieve the "dual-carbon" goal, scholars have conducted systematic research on the carbon effects of farmland. Currently, the research mainly focuses on the following areas: (1) estimation and analysis of carbon emissions and carbon uptake on farmland at the national or regional level, such as Dioha estimated the total greenhouse gas emissions from the Nigerian agriculture sector [11], Hemingway estimated the greenhouse gas emissions from crops in an Indian village [12], Li calculated the carbon emissions and carbon uptakes on the Qinghai-Tibet Plateau in China [13], and Dyer assessed energy-based greenhouse gas emissions from Canadian agriculture [14]; (2) factors influencing carbon effects on farmland, such as agricultural inputs [15,16], land-use changes [17,18], planting and cultivation patterns [19,20], and straw return [21], and driving mechanisms of carbon emissions in farmland, including agricultural technology [22], national policies [23], and markets [24]; (3) predictions of carbon emissions by grey prediction models [25,26], neural networks [27,28], the STIRPAT model [29], and estimations of carbon-emissions reduction potential [30]; and (4) carbon-uptake enhancement and emissions-reduction pathways and policies, including integrated crop-livestock systems [31], organic farming [32], conservation tillage [33,34], intermittent water-saving irrigation, agricultural investments [35], and a series of policy recommendations [36].…”