The Synergistic Development of Agricultural Chemical Emissions Reduction and Food Production Based on Decoupling and LMDI Models: A Case Study of Shandong Province

Abstract

Agricultural chemicals are indispensable in the process of traditional grain production and are also a major contributor to agricultural carbon emissions. Exploring the relationship between agricultural chemical carbon emissions and grain production is of significant importance for reducing agricultural emissions and promoting environmentally friendly grain production. To this end, this study employs the Tapio model and the LMDI factor decomposition model to analyze the decoupling relationship between agricultural chemical carbon emissions and grain production in Shandong Province—a typical grain-producing region in northern China—from a production perspective, focusing on the period from 2011 to 2023. The results indicate that during this period, Shandong Province achieved improvements in grain production technology, leading to a gradual improvement in the decoupling relationship between grain production and agricultural chemical carbon emissions. The factors influencing agrochemical carbon emissions during grain production initially shifted from being suppressed by output scale effects and promoted by technological effects to being suppressed by technological effects and promoted by output scale effects. Ultimately, synergistic development was achieved in Shandong Province by reducing agrochemical emissions and increasing grain production. This study provides a theoretical basis for synergistic development in agrochemical emission reduction and grain yield enhancement, while also offering a new perspective for research on reducing emissions during grain production.

1. Introduction

Since the concepts of carbon peaking and carbon neutrality were proposed, the issue of environmental degradation caused by greenhouse gases has drawn increasing public attention [1]. Agricultural production activities are a significant contributor to the increase in greenhouse gases [2], accounting for up to a quarter of global emissions [3]. China, as one of the world’s largest agricultural producers and consumers, leads the world in agricultural greenhouse gas emissions [4], accounting for approximately one-tenth of total carbon dioxide emissions and over fifty percent of total non-carbon dioxide emissions [5,6]. Thus, agricultural production activities have become a key factor constraining China’s ability to achieve its dual carbon goals, while also indicating that there is significant potential for reducing emissions in agricultural production [7].

Driven by population growth, China’s demand for food has been growing steadily. Ensuring food security and maintaining stable food supplies in the country have become urgent issues to address. As a result, the proportion of land allocated to food crop cultivation accounts for a significant portion of the total arable land. According to the results of the Third National Land Survey, China’s total arable land area is 1.28 × 10⁸ hectares, of which 1.18 × 10⁸ hectares are dedicated to food crop cultivation, with grain cropland accounting for 92% of arable land. China’s grain yield per unit area is 6.87 × 10⁹ tons, with per capita grain production reaching 486 kg [8], exceeding the internationally recognized 400 kg grain security threshold [9]. It can be seen that grain production is the main contributor to agricultural carbon emissions in China. As early as 2013, carbon emissions from grain production accounted for approximately 30% of the country’s total agricultural carbon emissions, with indirect carbon emissions accounting for 57% [2]. In recent years, as grain production has continued to increase, so have carbon emissions from grain production [10]. Based on China’s actual conditions, agricultural chemicals in fertilizers, pesticides, and agricultural films account for approximately 80% of carbon emissions from grain cultivation [11]. Meanwhile, as mechanization levels have improved, traditional internal combustion diesel engines remain the primary power tools for agricultural production operations [12]. As an indispensable energy source in agricultural production, the use of agricultural diesel has also increased with advancements in agricultural technology [13,14]. Since China’s reform and opening up, carbon emissions from agricultural energy consumption have shown an upward trend, rising from 3002.32 tens of thousands of tons in 1979 to 237 million tons in 2018, an increase of nearly eightfold. As early as 2018, carbon emissions from energy consumption accounted for 27.18% of the total agricultural carbon emissions [15].

In agricultural production, the greenhouse gas carbon emissions resulting from the improper use of agricultural chemicals have long been a focal point of concern [16]. Moreover, the unique properties of chemicals such as pesticides, fertilizers, and plastic films mean that their use leaves residues in the soil, causing direct environmental harm [14,17]. Data indicate that non-point-source pollution from agricultural production exceeds that from the industrial sector, exerting severe adverse effects on the environment [18]. Additionally, increased carbon emissions can lead to climate deterioration and food security issues such as reduced grain production and quality [19]. Since the concept of food security was proposed in 1974, China has achieved significant progress, with the goal shifting from focusing solely on quantity to balancing quantity, quality, and green coordination [20]. Since the 18th National Congress in particular, there has been strong emphasis on the coordinated development between carbon emissions from agricultural chemicals in grain production and grain production itself [21]. In-depth research on the relationship between grain production and carbon emissions from agricultural chemicals is significant for protecting the environment, ensuring food security, and achieving the dual carbon goals [22].

Most research on agricultural carbon emissions has progressed as follows. Firstly, regarding the selection of carbon sources and their calculation, as previously stated by scholars [9], the mainstream methods for carbon emission accounting include the carbon emission factor method, life cycle assessment (LCA), the input–output method, and the actual measurement method. Among these, the carbon emission factor method is the most widely used as it is less complex than the LCA method, which involves calculating indicators and considering the impacts of each stage [23]. It also does not require the consideration of multiple constraints like the input–output method [24], nor does it require actual experimental measurements like the actual measurement method [25]. Compared to these methods, the carbon emission factor method has a lower threshold for use, fewer constraints, and is more practical [19,26]. Its primary advantage is the ability to precisely determine the carbon emissions of the specific carbon source under study, facilitating subsequent research. In this study, the selected carbon sources are the four primary carbon sources commonly used in agricultural production: fertilizers, pesticides, plastic films, and agricultural diesel. This ensures both the specificity and scientific rigor of the research [9,27].

Secondly, in terms of decoupling research, most scholars have focused on the decoupling relationship between agricultural carbon emissions and agricultural economic growth. Prior studies have mainly concentrated on the decoupling relationship between agricultural carbon emissions and the agricultural economy [28,29,30], or the decoupling relationship between agricultural energy consumption and agricultural economic growth [31]. Currently, there is little research on the decoupling relationship between agricultural carbon emissions and food production, and even less on the decoupling relationship between carbon emissions from agricultural chemicals and food production.

Finally, in studies on the decomposition of the drivers of agricultural carbon emissions, previous research [26,32,33,34] generally categorized these drivers into agricultural production efficiency (agriculture carbon emissions/agricultural production output), regional economic structure (agricultural production output/regional total production output), per capita economic level (regional production output/regional total population), urbanization effect (regional total population/rural population), and labor force size (rural population size) and so on. The selected drivers are mostly studied from a macro perspective, with limited consideration of micro-level factors related to agricultural production, resulting in an incomplete reflection of actual agricultural production conditions.

In summary, the contributions of this study are as follows. Given the limited research on the relationship between agricultural chemical carbon emissions and food production, this study proposes the hypothesis that agricultural chemical carbon and food production can be decoupled and relevant verification was conducted, which to a certain extent demonstrated their feasibility of synergistic development, also filling a gap in this area of research.

2. Materials and Methods

2.1. Study Area

As shown in Figure 1, Shandong Province (34°22–38°24′ N, 114°47–122°43′ E) is located on the eastern coast of China, covering an area of approximately 1.58 × 10⁵ km². It comprises 16 prefecture-level cities. It has a mild climate with abundant rainfall, typical of a warm temperate monsoon climate, with annual precipitation ranging from 550 to 950 mm and a temperature range from 11 to 14 °C, sufficient to meet the growth requirements of most crops [35]. The terrain is predominantly flat, with fertile soil, making it suitable for crop production. Shandong Province has consistently ranked among the top provinces in China in terms of grain planting area and is a major grain-producing province [36]. In 2023, Shandong Province’s grain production reached 56.553 million tons, accounting for 8.13% of China’s total grain production, ranking third in the country. The province’s grain production is relatively monotonous, primarily consisting of wheat and corn, with these two crops accounting for approximately 95% of the province’s total grain production [37].

2.2. Data Sources

To more accurately calculate and quantify carbon emissions from agricultural chemicals in Shandong Province, the pure nutrient content of nitrogen, phosphorus, and potassium fertilizers is used in this study to estimate fertilizer carbon emissions, ensuring the accuracy of the calculations. Additionally, Shandong Province underwent administrative restructuring in 2019, with Laiwu City being dissolved and merged into Jinan City; thus, to maintain the balance and rationality of the data, we consolidated Laiwu City’s data into Jinan City’s data. All relevant data were sourced from the “Shandong Province Statistical Yearbook” and the statistical yearbooks of various prefecture-level cities in Shandong Province. In order to maintain consistency between provincial and municipal data, provincial-level data for certain years was adjusted by aggregating data from 16 prefecture-level cities.

2.3. Data Analysis

The software used in this study included Excel 2021, Stata 18, ArcGIS 10.8, and Origin 2024, which were employed to analyze the data. The carbon emission factor method was used to calculate the carbon emissions from agrochemicals in each region. The Tapio model was utilized to calculate the decoupling relationship between agricultural chemicals and food production. The LMDI factor decomposition model was employed to conduct a quantitative analysis of the factors influencing the carbon emissions from agricultural chemicals in food production.

2.4. Calculation of Carbon Emissions from Agrochemicals

The carbon emission calculation method, currently used by mainstream scholars, was used in this study. The carbon emission factor method was first proposed by the IPCC in 1996, as outlined in the IPCC National Greenhouse Gas Inventory Guidelines. After several revisions, the 2006 guidelines [38] were subsequently published. The 2006 version has now become the most widely adopted standard method, providing a more detailed framework for agricultural emission calculations (including crop cultivation, livestock farming, and agricultural soils). Therefore, this study combines the carbon sources required in China’s agricultural cultivation processes with the specific circumstances of China’s grain production. It takes into account the fact that fertilizers, pesticides, and agricultural films account for approximately 80% of carbon emissions from grain production activities [11], as well as the fact that agricultural diesel is an indispensable energy resource in grain production (used in activities such as sowing, plowing, and harvesting) [39]. Based on this, nitrogen fertilizers, phosphorus fertilizers, potassium fertilizers, compound fertilizers, pesticides, agricultural films, and agricultural diesel were selected as carbon sources to calculate the carbon emissions from agricultural chemicals in grain cultivation. The various carbon sources and carbon emission factors are shown in

Table 1. Carbon emission factors for agricultural chemicals [40,41].

Carbon Source	Carbon Emission Factor t C/t	Meaning
Nitrogen Fertilizers	0.42 t C/t	Nitrogen fertilizer application rate converted to pure amount
Phosphorus Fertilizers	0.44 t C/t	Phosphorus fertilizer application rate converted to pure amount
Potash Fertilizers	0.18 t C/t	Potassium fertilizer application rate converted to pure amount
Compound Fertilizers	0.48 t C/t	Compound fertilizer application rate converted to pure amount
Pesticides	3.39 t C/t	Pesticide application rate
Agricultural Films	6.2 t C/t	Agricultural film application rate
Agricultural Diesel	0.59 t C/t	Agricultural diesel consumption

, and the formula is constructed as follows:

$${\mathrm{C}}_j= \sum {\mathrm{Q}}_{ji}\times {\mathrm{l}}_i\times {\displaystyle \frac{{\mathrm{S}}_j}{{\mathrm{G}}_i}}$$

(1)

(1) In the formula, ${\mathrm{C}}_j$ represents the carbon emissions from agricultural chemicals used in grain production in Shandong Province or its prefectural-level cities, where ${\mathrm{Q}}_{ji}$ represents the consumption of carbon sources of the i-th category in Shandong Province and its cities, ${\mathrm{l}}_i$ is the carbon emission coefficient for the i-th type of carbon source, ${\mathrm{S}}_j$ represents the grain planting area in Shandong Province or its prefectural-level cities, and ${\mathrm{G}}_i$ represents the crop planting area in Shandong Province or its prefectural-level cities (where i = 1, 2, 3, … 7, representing nitrogen, phosphorus, potassium, compound fertilizer, pesticides, agricultural film, and agricultural diesel, respectively).

2.5. Tapio Model

Since 2005, scholars have provided an in-depth and systematic explanation of the application of the Tapio theory and applied it to the transportation sector, calculating the decoupling relationship between EU freight volume and economic growth. This approach has since been widely adopted across various fields, including transportation, economics, agriculture, energy, and the environment. The advantage of this method lies in its flexibility of application, which avoids the need to select a base period. It can not only describe the long-term decoupling relationship between agricultural input carbon emissions and food production, but can also reflect changes in decoupling across different periods [9]. Drawing on previous applications and incorporating research from scholars in the agricultural sector, the Tapio theory was employed in this study to investigate the decoupling relationship between agricultural chemical carbon emissions and food production. There are eight types of decoupling relationships, with specific details outlined in

Table 2. Types of decoupling and their meanings.

ΔC	ΔQ	Decoupling Index (E)	Decoupling Status	Implications
<0	>0	E < 0	Strong decoupling	Increased grain production and reduced carbon emissions from agricultural chemicals.
>0	>0	0 < E < 0.8	Weak decoupling	Carbon emissions from agricultural chemicals and food production are both increasing, with food production growing faster than carbon emissions from agricultural chemicals.
>0	>0	0.8 < E < 1.2	Expansive coupling	Carbon emissions from agricultural chemicals and food production are both increasing, and the rates of increase are roughly consistent.
>0	>0	E > 1.2	Expansive negative decoupling	Carbon emissions from agricultural chemicals and food production are both increasing, with food production growing at a slower rate than carbon emissions from agricultural chemicals.
>0	<0	E < 0	Strong negative decoupling	Increased carbon emissions from agricultural chemicals used in food production, with reduced food production.
<0	<0	0 < E < 0.8	Weak negative decoupling	Carbon emissions from agricultural chemicals and food production are both decreasing, with food production decreasing at a faster rate than carbon emissions from agricultural chemicals.
<0	<0	0.8 < E < 1.2	Recessive coupling	Carbon emissions from agricultural chemicals and food production are both decreasing, and the rates of decrease are roughly consistent.
<0	<0	E > 1.2	Recessive decoupling	Carbon emissions from agricultural chemicals and food production are both decreasing, with food production decreasing at a slower rate than carbon emissions from agricultural chemicals.

.

Based on the above analysis and drawing on the calculation methods of relevant scholars [11], we calculated the decoupling model between carbon emissions from agricultural chemicals and grain production in Shandong Province and its cities and districts, and established the following equation:

$$E = {\displaystyle \frac{{(C}_t-C_0)/C_0}{{(Q}_t-Q_0)/Q_0}} = {\displaystyle \frac{\Delta \mathrm{C}/\mathrm{C}}{\Delta \mathrm{Q}/\mathrm{Q}}}$$

(2)

(2) In the equation, $C_t$ and $C_0$ represent the carbon emissions from agricultural chemicals emitted during grain production in period t and the base period, respectively; $Q_t$ and $Q_0$ represent the grain yield in period t and the base period, respectively; $\Delta C/C$ represents the rate of change in carbon emissions from agricultural chemicals emitted during grain production in a given period; and $\Delta Q/Q$ represents the rate of change in grain yield in a given period. E denotes the technological decoupling elasticity, i.e., the ratio of the rate of change in carbon emissions from agricultural chemicals to the rate of change in grain yield.

2.6. LMDI Factor Decomposition Model

Since LMDI factor decomposition was first proposed in the early 1980s, it has been widely applied across various fields. Based on the type of decomposition, it can be categorized into additive decomposition and multiplicative decomposition. Multiplicative decomposition focuses more on the contribution of each factor to the decomposition quantity, while additive decomposition emphasizes the difference in the impact of each factor on the decomposition quantity [42]. Based on this, this study draws on the research of previous scholars and modifies the formula [43,44] to use the LMDI model to decompose carbon emissions from agricultural chemicals in the food sector as follows:

$$C = {\displaystyle \frac{C}{P}}\times {\displaystyle \frac{P}{S}} \times S$$

(3)

(3) where C represents carbon emissions from agricultural chemicals used in grain production, P represents grain yield, S represents grain planting area, and ${\displaystyle \frac{C}{P}}$ represents the technological effect (the amount of carbon emissions from agricultural chemicals per unit of grain produced, which can be interpreted as the level of grain production technology; the higher the grain production technology, the lower the volume of agricultural chemicals required per unit of grain produced, resulting in lower carbon emissions. It can also be interpreted as the degree of dependence on agricultural chemicals in grain production). ${\displaystyle \frac{P}{S}}$ represents the output effect (grain yield per unit of sown area, which can be interpreted as grain output level or land productivity; the higher the output level, the more grain produced per unit of sown area), and S represents the scale effect (the larger the grain-sown area, the greater the input of agricultural chemicals may be).

Let ${\displaystyle \frac{C}{P}}$= A, ${\displaystyle \frac{P}{S}}$= B. Clearly, C, P, and S are positive numbers, and the domain of the lnx function is (0, +∞), $\Delta \mathrm{C}$ which can be positive or negative.

To this end, the formula is transformed as follows:

$$\left|\Delta C\right| = \left|C_t-C_0\right| = |A_t\times B_t\times S_t-A_0\times B_0\times S_0|$$

(4)

(4) In the equation, $C_t$ and $C_0$ represent the carbon emissions from agricultural chemicals in period t and the base period, respectively. $A_t$, $B_t$, and $S_t$ represent the technical effect, output effect, and scale effect in period t, respectively. $A_0$, $B_0$, and $S_0$ represent the technical effect, output effect, and scale effect, respectively, of the base period.

After transforming Equation (4), taking the logarithm of both sides of the equation yields

$$\ln (|\Delta C|)=\ln (|C_t-C_0|)=\ln (|A_t\times B_t\times S_t-A_0\times B_0\times S_0|)={\displaystyle \frac{\mathrm{l}\mathrm{n}(A_t\times B_t\times S_t)}{\mathrm{l}\mathrm{n}(A_0\times B_0\times S_0)}}$$

(5)

At the same time, transforming Equation (5) yields

$|\Delta C|$ = ${\displaystyle \frac{|C_t-C_0|}{\mathrm{l}\mathrm{n}(|C_t-C_0|)}}\times$ ln(${\displaystyle \frac{A_t}{A_0}}+{\displaystyle \frac{B_t}{B_0}}+{\displaystyle \frac{S_t}{S_0}}$). It can be concluded that $|\Delta \mathrm{C}|$ represents the absolute value of the change in carbon emissions from agricultural chemicals caused by technological effects, output effects, and scale effects, while ${\displaystyle \frac{|C_t-C_0|}{\mathrm{l}\mathrm{n}(|C_t-C_0|)}}\times$ [ln($A_t$) − ln($A_0$)] represents the change in carbon emissions from agricultural chemicals caused by changes in technological effects. ${\displaystyle \frac{|C_t-C_0|}{\mathrm{l}\mathrm{n}(|C_t-C_0|)}}$ $\times$ [ln($B_t$) − ln($B_0$)] represents the change in carbon emissions from agricultural chemicals caused by changes in output effects, and ${\displaystyle \frac{|C_t-C_0|}{\mathrm{l}\mathrm{n}(|C_t-C_0|)}}\times$ [ln($S_t$) − ln($S_0$)] represents the change in carbon emissions from agricultural chemicals caused by changes in scale effects.

3. Results

3.1. Carbon Emissions from Agricultural Chemicals and the Timing of Grain Production in Shandong Province

As shown in Figure 2, based on the temporal trends in grain production and carbon emissions from agricultural chemicals in Shandong Province, the temporal characteristics of grain production and carbon emissions from agricultural chemicals can be divided into two stages. The first stage, from 2011 to 2016, was characterized by a decline in grain production and a slow reduction in carbon emissions from agricultural chemicals. The second stage, from 2017 to 2023, was characterized by an increase in grain production and a rapid reduction in carbon emissions from agricultural chemicals.

In the first stage, the level of grain production technology is relatively low, and there is a high dependence on agricultural chemicals in the grain production process. This is reflected in a reduction in carbon emissions from agricultural chemicals used in grain production, which is typically accompanied by a decrease in grain planting area and yield. At this stage, the carbon emissions from agricultural chemicals used in grain production remain at a relatively high level, indicating significant potential for emission reduction. The underlying cause of this phenomenon is that, due to poor soil quality and relatively low agricultural production technology levels, agricultural inputs are substantial, particularly the costs associated with agricultural chemicals, leading to low grain production yields and, consequently, reduced enthusiasm among producers to cultivate grain [45]. As a result, Shandong Province exhibits an overall reduction in grain cultivation area, decreased grain production, and lower carbon emissions from agricultural chemicals.

Following improvements in grain production technology during the second phase, Shandong Province reduced its reliance on agricultural chemicals in grain production, stabilized grain planting areas, steadily reduced carbon emissions from agricultural chemicals used in grain production, and achieved steady growth in grain output. This was attributable to the implementation of the “National Plan for Adjusting the Structure of Crop Production (2016–2020)” issued by the Ministry of Agriculture in 2016, the introduction of subsidies for soil fertility protection proposed by the Ministry of Finance [46], the strategic deployment of “storing grain in the land and in technology” [47], and the results of high-standard farmland construction. These approaches require all regions to adjust their planting structures; achieve the coordinated development of grain, cash crops, and fodder; increase grain production; reduce production costs; vigorously promote the coordinated development of crop production and ecology; improve the utilization rate of agricultural chemicals; and promote the resource utilization of agricultural waste. In 2017, the “Opinions on Innovating Mechanisms and Systems to Promote Green Agricultural Development” were issued, requiring regions to further coordinate the relationship between agricultural production and the environment, reduce chemical inputs while minimizing the consumption of related inputs, and maximize the avoidance of environmental pollution caused by agricultural production. The goals were to increase production without increasing pollution, increase production without increasing carbon emissions, and reducing costs while improving efficiency. These policies provided Shandong Province with a roadmap for action. The province actively implemented and formulated relevant policies, and through these efforts, grain production levels improved and carbon emissions from agricultural chemicals were further reduced [48].

3.2. Analysis of the Decoupling Relationship Between Carbon Emissions from Agricultural Chemicals and Grain Production in Shandong Province

Drawing on relevant research methods used by previous scholars [49] and combining them with China’s policies on grain cultivation over the years, and in order to align with the key time points of the country’s key five-year strategic plans, the study period was divided into three time segments: 2011–2015, 2016–2020, and 2021–2023.

Figure 3 shows that from 2011 to 2015, in 16 regions in Shandong Province except for Zibo City and Weihai City, the decoupling between agricultural chemical carbon emissions and grain production showed a recessive coupling trend, indicating that both agricultural chemical carbon emissions and grain production decreased, and the magnitude of the decrease in agricultural chemical carbon emissions and grain production was roughly the same. In the cities of Jinan, Qingdao, Zaozhuang, Yantai, Weifang, Jining, Tai’an, Rizhao, Dezhou, and Liaocheng, the decoupling between agricultural chemical carbon emissions and grain production exhibited a weak negative decoupling trend. This indicates that during this period, both agricultural chemical carbon emissions and grain production decreased, with the decline in grain production being greater than that in agricultural chemical carbon emissions. Dongying City and Binzhou City exhibit an expanding negative decoupling, indicating that, at this time, both agricultural chemical carbon emissions and grain production are increasing, with the increase in grain production being smaller than the increase in agricultural chemical carbon emissions. Linyi exhibits a recessive decoupling relationship, indicating that, at this time, both agricultural chemical carbon emissions and grain production are decreasing, with the decrease in grain production being smaller than the decrease in agricultural chemical carbon emissions. Heze exhibits a weak decoupling relationship, indicating that, at this time, both agricultural chemical carbon emissions and grain production are increasing. The growth rate of grain production is greater than the growth rate of carbon emissions from agricultural chemicals used in grain production. Since most regions have poor decoupling relationships, Shandong Province exhibits strong negative decoupling. Overall, at this time, most cities in Shandong Province have relatively backward grain production technologies and a high dependence on agricultural chemicals. Therefore, Shandong Province as a whole exhibits weak negative decoupling.

The relationship between carbon emissions from agricultural chemicals required for grain production and grain output in various regions of Shandong Province from 2016 to 2020 shows that the decoupling status has improved in most cities. Except for Qingdao, Zibo, Yantai, Weifang, Tai’an, Weihai, and Rizhao, which are in a state of recessive decoupling, the remaining nine cities and districts are in a state of strong decoupling. This indicates that in the seven cities including Qingdao, both grain production and the carbon emissions from agricultural chemicals used in grain production are decreasing. However, the rate of decrease in carbon emissions from agricultural chemicals is faster than the rate of decrease in grain production. The remaining nine cities have achieved both increased grain production and reduced carbon emissions, which is also a desirable state overall [50]. This is attributable to the promulgation of the Environmental Protection Tax Law of the People’s Republic of China in 2016 and the Soil Pollution Prevention and Control Law of the People’s Republic of China in 2018, which provide important policy support for advancing carbon emission reduction in agriculture. Regions across Shandong Province have gradually adjusted their grain planting structures, strengthened the resource utilization of agricultural waste, and implemented measures such as straw return to fields and manure resource utilization. These measures have effectively reduced the use of agricultural chemicals while significantly lowering the input costs of such chemicals. Additionally, with advancements in agricultural technology, the adoption of more environmentally friendly production techniques and management models has further facilitated the decoupling of carbon emissions from agricultural chemicals and grain production [51]. Therefore, Shandong Province, as a whole, demonstrates a strong decoupling trend.

From 2021 to 2023, Shandong Province and its 16 cities and districts showed strong decoupling, except for Zaozhuang City, which showed expansive coupling. This indicates that, at this time, grain production technology in most areas of Shandong Province improved significantly.

3.3. Analysis of Carbon Emissions from Agricultural Chemicals Used in Food Production

In the process of food production, changes in planting scale and land productivity may affect the carbon emissions of agricultural chemicals [9,52]. How to coordinate and promote the relationship between food production and carbon emissions from agricultural chemicals is of great significance for promoting green transformation in agriculture, alleviating environmental pressure, ensuring food security, and promoting sustainable agricultural development [53].

From the above analysis, it can be seen that the decoupling relationship between grain production and agricultural chemical use in Shandong Province gradually improved from 2011 to 2023, indicating positive results in its transition to green agriculture. Below, we will break down the indicators related to grain production and carbon emissions from agricultural chemicals in Shandong Province to explore the underlying reasons. This will not only clarify the relationship between grain production and carbon emissions from agricultural chemicals in Shandong Province, but also provide a reference for other regions.

As shown in

Table 3. Analysis of carbon emission drivers for agricultural chemicals used in food production.

Year	2011–2015				2016–2020				2021–2023
Area	Total Effect (Tons)	Technical Effect (Tons)	Output Effect (Tons)	Scale Effect (Tons)	Total Effect (Tons)	Technical Effect (Tons)	Output Effect (Tons)	Scale Effect (Tons)	Total Effect (Tons)	Technical Effect (Tons)	Output Effect (Tons)	Scale Effect (Tons)
Shandong	−186,502.25	190,774.27	−38,665.62	−338,611.04	−418,471.8	−787,814.6	−2775.67	372,118.69	−142,241.75	−230,901.90	76,068.61	12,591.55
Jinan	−11,359.8	9455.99	−3012.06	−17,803.72	−33,639.56	−39,481.88	−1821.95	7664.27	−6830.13	−11,770.81	4257.7	682.91
Qingdao	−14,090.75	20,543.62	−5839.67	−28,794.69	−32,368.45	−31,887.49	4973.97	−5454.93	−10,462.06	−14,568.95	3146.96	959.94
Zibo	−20,102.27	3090.84	−6361	−16,832.12	−22,819.84	−22,251.81	−109.95	−458.08	−2812.88	−4682.47	1533.12	336.47
Zaozhuang	−3647.2	11,093.31	−2677.3	−12,063.22	−5054.96	−16,466.33	−639.16	12,050.52	2047.41	−452.43	1978.76	521.08
Dongying	24,724.41	8389.79	−6077.55	22,412.17	−27,043.48	−54,325.43	−9954.39	37,236.34	−3505.83	−10,110.14	6237.72	366.59
Yantai	−61,344.22	31,883.17	−20,583.65	−72,643.74	−51,986.88	−44,900.66	3090.6	−10,176.81	−12,948.94	−20,002.99	5706.84	1347.22
Weifang	−43,000.56	52,076.69	−31,821.94	−63,255.37	−58,693.59	−50,109.43	9577.68	−18,161.84	−16,889.44	−28,932.40	10,797.71	1245.24
Jining	−3836.49	13,768.4	−1760.46	−15,844.41	−28,276.63	−48,258.21	−9656.14	29,637.72	−8393.49	−13,272.29	4030.39	848.44
Tai’an	−2305.78	16,045.01	1742.98	−20,093.77	−5062.42	−2220.21	−5603.9	2761.67	−1375.79	−4120.58	2175.26	569.54
Weihai	−37,264.13	7991.87	1009.43	−46,265.42	−13,856.23	−4756.42	3552.17	−12,651.97	−2738.33	−8082.73	1785.87	3558.52
Rizhao	−18,715.81	19,681.03	−12,211.24	−26,185.61	−57,570.52	−51,271.23	10,044.97	−16,344.27	−6347.06	−8633.08	208.78	2077.24
Linyi	−54,087.12	−11,096.88	7278.35	−50,268.59	−44,725.88	−47,795.86	7859.11	−4789.14	−8627.03	−14,219.88	4740.12	852.73
Dezhou	−977.13	39,149.93	−19,448.59	−20,678.47	−40,541.9	−87,739.46	−15,766.24	62,963.79	−25,745.19	−31,910.05	5823.59	341.29
Liaocheng	−1813.28	16,575.73	−2053.89	−16,335.13	−19,209.57	−57,425.19	10,115.49	28,100.14	−7535.94	−14,657.05	6570.96	550.14
Binzhou	9967.38	8937.53	−1061.34	2091.21	−14,991.27	−42,984.84	−9273.61	37,267.19	−12,242.16	−16,074.95	3184.21	648.57
Heze	12,855.34	−5190.35	33,251.51	−15,205.86	−11,536.44	−99,679.88	20,650.28	67,493.15	−14,521.63	−22,353.42	7410.65	421.16

, from the perspective of Shandong Province as a whole, the three major effects influencing the carbon emissions of agricultural chemicals used in grain production are as follows: from 2011 to 2015, the primary factors suppressing carbon emissions from agricultural chemicals in Shandong Province were the output effect and the scale effect, with the scale effect having a greater impact than the output effect. The primary factor promoting growth was the technological effect. Based on the above analysis, it can be concluded that the reduction in grain planting areas and the limited use of agricultural inputs were primarily due to issues related to grain production costs, leading to the scale effect and output effect becoming suppressing factors. From 2016 to 2020, the primary factors suppressing carbon emissions from agricultural chemicals in Shandong Province were the technology effect and output effect, while the primary factor driving their growth was the scale effect. This may be attributed to technological advancements in grain production and large-scale adjustments to crop structures during this period. From 2021 to 2023, the inhibiting factors for carbon emissions from agricultural chemicals in Shandong Province’s grain production were technological effects, while the driving factors were output effects and scale effects. This indicates that during this period, Shandong Province increased its grain planting scale while also focusing on improving land productivity, reflecting further advancements in grain production technology.

From an internal perspective, among the 16 regions within the province during the stage of relatively backward grain cultivation technology (2011–2015), except for Heze City showing weak decoupling and Linyi City showing declining decoupling with corresponding negative technical effects, the decoupling relationship between grain agricultural chemical carbon emissions and grain production in most other regions exhibited a negative decoupling, coupling state. At this time, the technical effects in other regions were all positive, indicating that the majority of provinces and cities had relatively low levels of grain production technology, primarily relying on extensive production methods with a high degree of dependence on agricultural chemicals. Additionally, from the perspective of total effects, Dongying, Binzhou, and Heze cities had positive total effects, while the remaining 13 regions were all negative. The positive total effect in Dongying City and Binzhou City is due to the expansion of planting scale despite technical limitations, while the positive total effect in Heze City is primarily due to improved land productivity. The other 13 regions are characterized by output effects and scale effects as the primary factors inhibiting the use of agricultural chemicals in grain production. This suggests that the reduction in carbon emissions from agricultural chemicals in grain production is primarily achieved by reducing grain planting scale and lowering land productivity, which is clearly not recommended [9]. Therefore, to increase land productivity while avoiding an increase in carbon emissions from agricultural chemicals, improving grain cultivation technology and adopting moderate-scale planting are key [54].

During the period from 2016 to 2020, when the decoupling relationship between carbon emissions from agricultural chemicals used in grain production and grain output was in effect, carbon emissions from agricultural chemicals used in grain production showed varying degrees of reduction. At this time, technological effects became the primary factors suppressing carbon emissions from agricultural chemicals used in grain production, indicating that over the past five years, grain production technologies in most cities across Shandong Province have significantly improved. The State Council approved the “National High-Standard Farmland Construction Master Plan (2011–2020)” in 2011. However, although regions have been diligently implementing the plan, due to differences in agricultural technology and economic development levels across regions, the lack of standardized evaluation criteria for construction has led to unsatisfactory results [55]. Therefore, by the end of 2015, Shandong Province had actively formulated policies such as the “Shandong Province High-Standard Farmland Construction Plan (2015–2020),” primarily based on the actual conditions of different regions within the province. The aim of these policies was to adjust crop structures by focusing on transforming medium- to low-quality farmland to promote intensive land use, promoting the resourceful utilization of agricultural waste, improving soil quality, and reducing reliance on agricultural chemicals to achieve the goal of high-standard farmland construction as quickly as possible. This also lays a solid foundation for increasing grain production while reducing emissions.

During the period from 2021 to 2023, except for an increase in carbon emissions from agricultural chemicals in Zaozhuang, all urban areas in Shandong Province achieved varying degrees of reduction in carbon emissions from agricultural chemicals used in grain production. Technological efficiency became the primary reason for this reduction, while production effects and scale effects emerged as driving factors. During this period, advancements in production technology led to the emergence of scale effects in grain cultivation, meaning that with technological support, grain production techniques improved, resulting in increased grain yields per unit area even as cultivation scales expanded. This is because the National High-Standard Farmland Construction Master Plan (2011–2020), the Shandong Province High-Standard Farmland Construction Plan (2015–2020), and the National High-Standard Farmland Construction Plan (2021–2030) have been largely implemented, with policy support from the latter [56]. With the basic completion of high-standard farmland construction [57], intensive farming on cultivated land has taken shape, soil quality has improved, crop structures have become more rational, and agricultural supporting infrastructure has been basically improved. As mechanization levels have increased, the utilization rate of agricultural waste resources has further improved, leading to a reduction in grain production costs [58]. This has significantly improved the phenomenon of farmland abandonment and greatly increased farmland utilization rates, raising the crop rotation index [59] and improving crop growth environments. This has reduced the reliance on agricultural chemicals in various regions [44]. At this stage, output effects and scale effects have become the primary drivers of carbon emissions from agricultural chemicals. Once crop structures are regionally optimized, the role of scale effects diminishes [60]. Overall, once grain planting technology reaches a certain level, reducing agricultural chemical inputs and increasing grain production can be achieved in a synergistic manner.

3.4. Analysis for Adjusting Crop Structure

Based on the previous analysis, we can see that Shandong Province undertook agricultural crop structure adjustments from 2016 to 2020, resulting in improved grain production levels across all regions, as shown in

Table 4. Dependence of grain production on agricultural chemicals in Shandong Province and its subregions.

Area	2016 Production Unit Grain Agricultural Chemical Emissions (Tons)	2020 Production Unit Grain Agricultural Chemical Emissions (Tons)	Changes in Grain Planting Area from 2016 to 2020
Shandong	0.0766	0.0617	+
Jinan	0.0668	0.0528	+
Qingdao	0.0865	0.0760	−
Zibo	0.0705	0.0547	−
Zaozhuang	0.0701	0.0606	+
Dongying	0.0892	0.0464	+
Yantai	0.1548	0.1295	−
Weifang	0.1329	0.1212	−
Jining	0.0567	0.0464	+
Tai’an	0.0513	0.0504	+
Weihai	0.2056	0.1973	−
Rizhao	0.1682	0.1086	−
Linyi	0.0861	0.0745	−
Dezhou	0.0466	0.0342	+
Liaocheng	0.0599	0.0493	+
Binzhou	0.0465	0.0344	+
Heze	0.0524	0.0386	+

. This is reflected in a reduction in the amount of agricultural chemicals emitted per unit of grain produced. We found that some regions expanded their production scale while others reduced it. This may be due to differences in geographical environment and social conditions across regions, leading to varying levels of grain production technology. Therefore, regions adjusted their planting scales based on their actual production conditions. Overall, during the initial phase of adjustments in 2016, regions with relatively high reliance on agricultural chemicals in grain production, such as the cities of Qingdao, Zibo, Yantai, Weifang, Weihai, Rizhao, and Linyi, chose to reduce their planting scales. This was because, on one hand, they were phasing out low-quality farmland to improve soil quality, and on the other were focusing on enhancing land productivity to lay the foundation for intensive production. In regions with relatively lower reliance on agricultural chemicals in grain production, such as Jinan, Zaozhuang, Jining, Tai’an, Dezhou, Liaocheng, Binzhou, and Heze, planting scales were expanded. This was because the quality of farmland was relatively good, and the regions had relatively mature grain planting technologies with a certain technical foundation. Moreover, the planting structure was relatively reasonable, and the promotion of high-standard farmland construction was also relatively easy. Additionally, Dongying City also expanded its planting scale during the 2016–2020 period. However, by 2020, the city’s reliance on agricultural chemical carbon emissions for grain production had decreased by nearly half compared to 2016. This is mainly due to Dongying’s vigorous promotion of salt–alkali land reclamation to improve soil quality [61].

In general, when adjusting the scale of grain cultivation, adjustments should be made based on the actual conditions of the region, taking into full consideration the quality of arable land and the level of grain production in the region to achieve appropriate-scale production.

4. Discussion

4.1. Analysis of Emission Reduction Measures in Shandong Province

The investigation found that during the study period, Shandong Province improved its grain production technology through a series of efforts, causing technological effects to enhance emission reductions, simultaneously demonstrating improvements in both output effect and effect of scale. The following is an analysis of some relevant measures taken by Shandong Province:

In view of the high cost of grain production and the low enthusiasm of relevant producers for production, the main implementation measures are as follows: increasing the enthusiasm of grain producers by issuing grain subsidies to expand the scale of grain production and increase grain production [62]; vigorously promote the strategy of returning straw to the field, and establish a four-in-one collection, storage and transportation mechanism of “enterprises + township collection and storage centers + village-level collection points + farmers”, according to The environment and development conditions of each region, and relying on local manufacturing advantages, develop and promote agricultural machinery and equipment related to the recycling of crop straw, improving the straw recycling rate [63]; vigorously promote the strategy of manure resource utilization through government guidance [64], and organize and coordinate a series of related cooperation work between farms, growers, commercial organic fertilizer factories, manure treatment equipment manufacturers, etc., to build a complete manure treatment equipment Resource utilization system, for example, the government takes the lead in guiding organic fertilizer production companies to sign cooperation agreements with farmers to provide free manure treatment equipment to farmers. Farmers can use this to process manure into organic fertilizer. The produced organic fertilizer can be used by themselves or recycled by the company to offset the cost of equipment purchase. This not only greatly increases the enthusiasm of farmers to treat manure but also reduces the waste of resources and provides green food. It has provided reliable assistance to food production [65]; by establishing a complete film recycling, storage and transportation system, developing a “town-village-household” three-level recycling network model, and implementing a recycling reward mechanism, such as exchanging old mulch films for new food production inputs or providing cash subsidies and other incentive measures. At the same time, it is committed to researching products corresponding to the adjustment of the planting system and planting structure, which has greatly improved the mulching film recycling rate [66].

In terms of other favorable measures: In the farming system, vigorously implement the land conservation system that promotes a comprehensive agricultural measure system with biological measures and ecological technology as the main content, transform the traditional production method that mainly relies on expanding the planting area to increase grain production, and strive to improve the planting level, increase land utilization, increase the multiple cropping index, and improve High land productivity is used to increase production [67], while focusing on ecological and socio-economic benefits, and advocating that improving the quality of cultivated land goes hand in hand with production [68]; in land transformation, the government funds the construction and relies on high-standard farmland construction to create basic permanent farmland. On the one hand, it transforms low-quality production fields, and on the other hand, it invests in the construction of fields. Supporting agricultural production infrastructure [69] promotes moderate-scale operation and production [70,71,72]. This not only increases the popularity of agricultural mechanization and its precise adaptability [73] but also improves mechanized productivity. It also improves the utilization rate of related agricultural inputs and the utilization efficiency of agricultural diesel; in terms of planting technology, In order to improve the quality and increase in grain production, Shandong Province actively explores grain planting technology, and the government actively promotes related planting technologies (corn-soybean strip compound planting [74], wheat intercropping and corn high-yield and efficient mixing technology [75], fertilization guidance after expert soil testing and formula, etc.) and the promotion of special machinery, and provides some basic supporting services. This not only improves the utilization rate of land, but also increases the productivity of land [63]; in terms of land policy, in response to the problem of small-scale agricultural economic operators that are common in rural areas, the implementation of land transfer policies has been accelerated through land confirmation and other means. This not only solves the situation of large-scale rural abandonment and promotes intensive large-scale production [76], but also It has improved the utilization efficiency of agricultural inputs [77]; in terms of the industrial system, a sound and complete grain industry economic system has been established, a complete grain post-production sales service system has been established, and infrastructure construction such as grain circulation and processing have been established and improved. The government has taken the lead in organizing farmers and enterprises to carry out grain production and marketing cooperation to ensure stable grain acquisition channels and ensure farmers’ income. This move not only ensures the income of food producers, but also increases their enthusiasm for planting [78], ensuring the sustainability of food production.

The main impacts of the above measures on technology effects, output effects, and scale effects are as follows:

Scale effect: This is mainly due to the green subsidies provided by the government for related grain production, as well as the investment in related infrastructure and the establishment of a sound economic system for the grain industry. This not only significantly reduces the production costs of related grain producers, ensuring their income, but also increases their enthusiasm for grain cultivation and voluntarily expands the scale of grain cultivation. On the other hand, it is attributed to the development of new land suitable for grain cultivation through high standard farmland construction projects.

Output effect: Mainly due to the transformation of the farming system, emphasis is placed on improving land productivity to increase grain production, while phasing out old farmland and focusing on its transformation, committed to improving and restoring soil fertility, and promoting green planting technologies such as crop intercropping. At the same time, it relies on the improvement of relevant basic supporting measures in the construction of high standard farmland. On the one hand, it guarantees the basic growth and development environment in the grain production process, and on the other hand, it reduces grain loss caused by natural disasters, ensuring the yield of grain per unit. In addition, it is also attributed to the positive externalities brought by straw returning and manure resource utilization to improve soil conditions.

Technical effect: On the one hand, they mainly come from the return of straw to the field and the utilization of manure resources, soil testing formulas for the application of agricultural chemicals, the promotion of land intensive production and high standard farmland construction projects by favorable land policies, the improvement of related agricultural basic matching measures, and the increase in the utilization rate of related agricultural chemicals brought about by the popularization of agricultural mechanization. On the other hand, the come from the positive externalities brought about by improving soil conditions and crop intercropping.

Generally speaking, Shandong Province has given full play to the main role of the central government, from the front-end of grain production, to the supporting production management in the middle, and finally to the back-end sales services. The government has established a series of safeguard measures that are beneficial to grain production and agrochemical emission reduction. This not only provides interest protection for relevant producers and helps increase grain production, but also effectively reduces the carbon emissions of agrochemicals in the grain production process.

4.2. Pathways for Synergistic Development of Agricultural Chemical Emission Reduction and Food Production

Based on the findings of this study, Shandong Province ultimately achieved synergistic development in reducing agricultural chemical emissions and increasing grain production. Meanwhile, the empirical results specifically show a negative total effect, a negative technology effect, a positive output effect, and a positive scale effect. Below, we will analyze these effects in detail.

A negative technological effect means that while carbon emissions from agricultural chemicals used in grain production decrease, grain yields increase. This implies a reduction in the amount of carbon emissions from agricultural chemicals per unit of grain production. At the same time, fertilizers, pesticides, plastic films, and agricultural diesel are indispensable in grain production activities to meet the nutritional needs of grain crops, protect them from pest damage, shield them from weather-related disasters, and ensure the operation of agricultural machinery. To reduce their usage, it is necessary to adopt a strategy of “reducing consumption and conserving resources” to minimize their use at the source. Regarding the reduction in fertilizer usage, relevant government departments can formulate policies, provide agricultural subsidies, promote the resource utilization of agricultural waste, and incentivize grain producers to adopt green production methods such as straw return to fields and the resource utilization of livestock manure, thereby promoting the use of organic fertilizers to replace traditional agricultural fertilizers [79].

This not only avoids greenhouse gas emissions and resource waste caused by straw burning, but also reduces fertilizer usage and increases crop yields [80]. Research has shown that straw return to fields has significant potential to replace fertilizer [81] and can increase grain yields and improve economic benefits [82,83]. To reduce pesticide usage, integrated crop–livestock systems can be adopted or physical pest control methods can be employed to manage pests. To reduce plastic film usage, governments can establish policies for the recycling and reuse of plastic film to promote its recovery and reuse, thereby achieving the dual objectives of reducing usage and preventing secondary non-point-source pollution. Regarding the reduction in agricultural diesel usage, on one hand, the trend toward agricultural mechanization is inevitable; on the other hand, with the development of society, the economy, and science and technology, as agricultural mechanization levels improve and agricultural machinery is used on a larger scale, labor costs, economic costs, and time costs are significantly reduced, while the production utilization rate of farmland is increased [84,85]. Therefore, improving the level of agricultural mechanization is an inevitable trend. To implement mechanization more precisely, large-scale grain production is key, and efforts should be made to promote land intensification and scaling up while accelerating the implementation of land transfer policies [86].

A positive output effect indicates an increase in grain production per unit area of land, which may imply an increase in the cropping index of farmland. This could lead to increased input of agricultural chemicals and may also mean that the variety of crops grown on the same land increases, thereby increasing the production of straw from related grain crops. The government could formulate relevant policies, such as agricultural green production subsidies [3], to encourage farmers to return straw to the fields or use straw as feed before returning livestock manure to the fields [87]. Straw-based fertilization [16] can replace the application of fertilizers, promoting green production and increasing land productivity while simultaneously increasing soil organic carbon, achieving no-till farming, and reducing carbon emissions from agricultural chemicals per unit area [87,88,89], while also reinforcing the technological effects.

A positive scale effect means that expanding scale increases carbon emissions from agricultural chemicals. It should be noted that the expansion of grain planting areas may lead to the increased use of agricultural chemicals [6]. Therefore, when expanding grain production scales, there must be sufficient grain planting technology to ensure that the technical effects can offset the scale effects, thereby achieving environmentally friendly production while expanding production scale [90].

Overall, achieving low-carbon food production hinges on the inhibitory role of the technological effect. Relying on traditional methods to suppress emissions by reducing output and scale effects is inadvisable; instead, the technological effect should drive food production.

Finally, it is predicted that as grain planting levels improve and grain planting structures become more rational, scale effects should exhibit minimal fluctuations. In the future, output effects may become the primary driver of carbon emissions from agricultural chemicals in grain production, while technological effects will become the dominant factor in suppressing carbon emissions from agricultural chemicals in grain production.

4.3. Limitations and Future Work

This study calculated the carbon emissions from agricultural chemicals in grain production in a region by multiplying the proportion of grain planting area to the total crop planting area in the region by the carbon emissions from the total consumption of agricultural chemicals in the region. This calculation was then used to further calculate the level of grain production technology. While this approach has a certain degree of accuracy, the phenomena of soil carbon sequestration and biological carbon sequestration are widely present in nature, and this was not accounted for. Vegetation absorbs carbon from the air through photosynthesis [91], and grain crops are no exception [92]. In agricultural production, the use of organic fertilizers can increase crop yields while also promoting soil organic carbon sequestration and thereby reducing carbon emissions [93,94]. Proper tillage and crop rotation may also promote soil carbon sequestration [95]. Therefore, as this study only utilized the carbon emissions from the consumption of agricultural chemicals in food production to represent the carbon emissions released during the food production process, the carbon emissions from agricultural chemicals may have been overestimated, resulting in certain deficiencies in measurement accuracy. Future research could further incorporate biological carbon sequestration and soil carbon sequestration when calculating carbon emissions from agricultural chemicals during food production, enabling the more precise measurement of carbon emissions from agricultural chemicals in food production and a more accurate assessment of food production levels in a given region.

5. Conclusions

This study shows that increasing land productivity and expanding the cultivated land area may increase carbon emissions from agrochemicals in food production. However, this impact can be offset by improving food production techniques, particularly by significantly reducing the reliance on agrochemicals. Reducing agrochemical dependence leads to synergy between reducing agrochemical emissions and increasing food production.

The analysis of the overall situation in Shandong Province shows that when agricultural production technology levels are low, i.e., when reliance on agricultural chemicals is high, reducing land productivity and grain planting scale can significantly lower carbon emissions from agricultural chemicals. However, this is accompanied by a simultaneous decrease in both carbon emissions and grain production. Conversely, after agricultural production technology levels have significantly improved, even when pursuing both increased land productivity and expanded planting scale, it is still possible to achieve the synergistic development of increased grain production and reduced emissions from agricultural chemicals.

The analysis of the internal regional situation in Shandong Province shows that when production technology levels are relatively low and grain production relies heavily on agricultural chemicals, increasing the grain planting scale or land productivity at this time will result in net carbon emissions from agricultural chemicals due to the low inhibitory effect of technology. In summary, to achieve both increased grain production and reduced emissions, moderate-scale production must be maintained at the corresponding level of planting technology.