Evolution characteristics of rural carbon emissions in Northwest China from 2006 to 2019

China is faced with significant challenges in simultaneously promoting rural development and reducing carbon emissions. However, the issue of quantifying and addressing carbon emissions in rural areas has not been adequately addressed. Accurately quantifying these emissions is crucial for developing effective strategies to reduce carbon output. In this study, the historical evolution and spatial distribution of rural carbon emissions in northwestern China from 2006 to 2019 were evaluated across five key sectors: residential energy consumption, agricultural machinery, solid waste management, planting practices, and breeding industry activities. During this period, total carbon emissions in rural areas of northwest China steadily increased from 60.15Mt to 83.49Mt at an annual growth rate of 2.55%. Given the complex interplay between economic and social factors driving these changes, the future trajectory of rural carbon emissions remains uncertain. To analyze the underlying drivers behind regional variations in carbon emissions over time, we constructed an LMDI model which revealed that economic growth primarily contributed to regional increases in carbon output. Furthermore, due to a remarkable annual growth rate of 35.17% in renewable energy generation (such as photovoltaic and wind power), it can be inferred that if renewable electricity were included within our calculations for carbon emission statistics, northwest China’s rural areas achieved a state of being effectively ‘carbon-neutral’ by 2019 solely from a production-based perspective.


Introduction
Global warming poses a pressing challenge to society at large, prompting countries worldwide to adopt measures such as controlling greenhouse gas emissions and implementing a 'low-carbon development' strategy (Kosaka and Xie 2013, Cai et al 2015, Sarkodie and Strezov 2019).Concurrently, the continuous release of greenhouse gases heightens human vulnerability to various diseases, constituting a significant potential threat to public health (Georgescu et al 2014, Mora et al 2022, Fuhrman et al 2023).Developing nations are experiencing rapid growth in carbon dioxide emissions due to their expanding economies.As one of the largest emitters among developing countries (Liu et al 2015, Zhang and Da 2015, Mora et al 2018, Liu et al 2022a, 2022b), China has set forth its commitment to peak emissions before 2030 and achieve carbon neutrality by or before 2060, that is, to offset its greenhouse gases by energy conservation and emission reduction to achieve relative 'zero emission.'Consequently, analyzing the spatiotemporal variation of carbon emissions becomes crucial for effectively implementing China's dual-carbon plan and contributing towards global emission reduction.
Historically, China's focus on energy conservation and emission reduction has primarily centered around national, provincial, and city levels (Zhang and Li 2021).However, research on rural carbon emissions has been predominantly limited to subjective evaluations without quantitative analysis.To address this gap, the Input-Output Model was employed to compare the carbon emissions of urban and rural residents in China (Zhang et al 2014), revealing that per capita carbon emissions for urban residents have shown a slower rate of increase compared to their rural counterparts since 2004.Notably, rural residents have experienced a significant increase in carbon emissions.Previous studies have calculated carbon emissions from various sectors including construction, transportation, food consumption, household consumption, agriculture, and tourism (Lin et al 2015, Xu and Lin 2017, Lenzen et al 2018, Tan et al 2018, Dong et al 2019, Liu et al 2021).Due to substantial disparities in population distribution, industrial structure, energy sources/types/efficiency as well as environmental awareness between urban and rural areas, the types and scale of carbon emissions differ significantly.While the countryside covers a large area with numerous inhabitants, it may exhibit more unconcentrated population distribution which hinders effective allocation and efficient utilization of energy resources (Liu et al 2017).The IPCC method was utilized to calculate China's rural carbon emissions which indicated an inverted U-shaped trend when biomass energy sources were ignored (Zhang et al 2010).
Energy use is the primary driver of greenhouse gas emissions in rural areas (Jakob et al 2014).Previous studies have analyzed carbon emission sources, such as coal, gasoline, oil, and natural gas (Xu et al 2014, Li et al 2015, Liu and Xiao 2018).However, the energy consumption structure in rural regions differs significantly from urban areas.Unlike urban areas where commercial energy sources like coal, gasoline, and diesel are predominantly used, biomass energy sources such as straw and firewood accounted for the majority of rural residents' energy consumption before 2007.Afterward, there was a significant increase in the proportion of commercial energy use in rural areas (Zhang and Yang 2009).To analyze the temporal heterogeneity characteristics of carbon emissions from rural energy consumption in China between 2000 to 2018 (Zhang and Li 2021), both Quadrant diagram method and Two-weighted proportional Theil index were employed.The study revealed substantial variations in the level and growth rate of carbon emissions from rural energy consumption among provinces.Furthermore, due to technological development lagging behind compared to urban regions, various types of energies exhibit lower efficiency levels resulting in higher greenhouse gas emissions when consuming an equivalent amount of energy.
Rural agriculture and animal husbandry significantly contribute to greenhouse gas emissions.The combustion of crop straw and biomass releases a substantial amount of greenhouse gases, including CO 2 and nitrogen oxides (Sun et al 2016).Methane generated from livestock breeding is inadequately utilized and released into the environment in large quantities (Li et al 2016).Moreover, the lack of environmental awareness among rural residents impedes the implementation of carbon emission reduction measures in rural areas (Boon-Falleur et al 2022), thereby limiting the efficacy of environmental policies.Due to disparities in energy structure, industrial composition, and ecological perspectives between urban and rural regions mentioned above, conventional quantitative analysis at national, provincial, and city levels fails to accurately depict the historical changes and structural characteristics of rural carbon emissions.
Despite fossil fuels remaining the primary source of carbon emissions, China's energy structure has experienced a rapid increase in new energy sources due to the dual policy drive of atmospheric environmental governance and carbon emission reduction.By 2022, wind and photovoltaic power generation had increased to 8.6% and 4.8%, respectively, in China.The International Energy Agency predicts that by 2027, photovoltaic and wind power generation will account for 22.2% and 14.4% of global energy supply (with coal at 20.9% and natural gas at 19.1%) (IEA 2022).As wind and solar resources are mainly concentrated in rural areas, rapidly growing rural new energy power generation has become a major influencing factor on regional carbon emissions.The reduction potential of new energy sources have attracted widespread attention from academia and government departments alike as renewable sources such as solar or wind energies continue their expansion towards becoming dominant factors in achieving carbon neutrality (Li et al 2014a, 2014b, Mou et al 2017, Zou et al 2021).
The driving factors of carbon emissions in rural and urban areas exhibit significant variations due to disparities in social development and energy consumption patterns.Structural Decomposition Analysis (SDA) and Index Decomposition Analysis (IDA) are the most commonly employed research methods for analyzing these driving factors (Hang et al 2019).SDA investigates the relationship between impact and consumption activities, while IDA examines the association between impact (such as energy, environment, or employment) and production levels.Although SDA yields more precise results, its application necessitates an input-output model as a foundation, thereby limiting its applicability (Su and Ang 2014).Conversely, IDA encompasses Laspeyres and Divisia methods that offer advantages in terms of data collection, ease of use, and further analysis of structural effects (Ang et al 2015), rendering it more widely utilized.For instance, the Divisia index method's LMDI approach has been employed to decompose and analyze driving factors of carbon emissions in Spain, China, and Pakistan respectively (Cansino et al 2015, Lin and Raza 2019, Yang et al 2020).However, there is currently no established solution for specifically analyzing the model of driving factors behind carbon emissions in rural areas at the provincial level.
The northwest region of China, which encompasses approximately one-sixth of the country's total area (3.04 × 10 6 km 2 ), has witnessed significant advancements in agriculture and animal husbandry.Owing to its elevated altitude, this region boasts abundant renewable resources such as solar and wind energy.In order to formulate effective strategies for carbon emissions reduction, it is imperative to consider the carbon footprint of this region.Household energy consumption in this area exhibits lower-than-average carbon emissions due to relatively slower economic development and lower living standards compared to other parts of China.However, with the implementation of national policies like the Western Development Strategy (Xu et al 2019), the economic of this region has experienced rapid growth in recent decades, potentially leading to future increases in residents' household energy-related carbon emissions (Li et al 2014a(Li et al , 2014b)).Therefore, it is crucial to explore alternative clean energy sources like solar and wind power as substitutes for traditional sources in order to mitigate future carbon emissions effectively.Existing research on rural carbon emissions within provincial regions (such as Xinjiang, Hubei, Zhejiang and Henan) primarily focuses on agricultural carbon emission intensity and overall quantity without providing a comprehensive analysis or research on rural carbon emission structure within provincial regions (Fan et al 2016, Tian and Wang 2020, Xu et al 2022, Wen et al 2023).
Additionally, high carbon emissions, a significant contributor to climate change, have emerged as a prominent issue in the northwest China (Tian et al 2019).For instance, Xinjiang has experienced an increased frequency of environmental problems such as glacier melting and sandstorms (Shi et al 2007).To mitigate the grave threat posed by the greenhouse effect on the climate and environment in the northwest China, it is imperative to accurately quantify regional carbon emissions and explore effective measures for emission reduction.
By employing the baseline method outlined in the IPCC National Greenhouse Gas Inventory Guidelines and incorporating localized parameter characteristics proposed in China's Provincial Greenhouse Gas Inventory Compilation Guidelines, a comprehensive analysis of rural carbon emissions from 2006 to 2019 in northwest China was conducted.The objective of this analysis was to examine the regional characteristics of carbon emission structure and identify the factors driving rural carbon emissions.Importantly, our study also took into account the significant scale of renewable electricity construction and utilization in northwest China when analyzing changes in provincial spatiotemporal patterns of rural carbon emissions.The findings presented here can serve as a valuable reference for developing tailored low-carbon emission reduction strategies that align with local conditions across the five provinces of northwest China.

Research object and data source
Covering an area of 3.12 million square kilometers, the northwest region of China is characterized by vast plateaus, deserts, and the Gobi Desert (as shown in figure 1).It experiences an arid and semi-arid climate.Over the period from 2006 to 2019, rapid urbanization has led to a decline in the rural population of the northwest region from 64.36 million to 46.99 million.However, this period also witnessed significant economic growth as GDP increased from 1069.7 billion yuan to 4604.1 billion yuan, resulting in improved living standards for residents.In terms of industrial structure in rural areas during 2006, primary industry accounted for 29.42%, secondary industry accounted for 46.12%, and tertiary industry accounted for 24.46% in the northwest region.The primary industry mainly focused on agriculture, with planting and breeding activities comprising over 95% of its contribution.The primary sources of energy consumed by rural residents are coal and oil.
The rural areas in northwest China possess abundant renewable resources and demonstrate immense potential for solar energy utilization.With an annual sunshine duration of over 3200 h and a yearly total radiation ranging from 6690 to 8360 MJ m −2 a −1 , this region boasts the most abundant solar energy resources in China.Moreover, China possesses vast wind energy reserves totaling 3.23 × 10 8 MW, with an exploitable capacity of 2.53 × 10 7 MW.In this area, the capacity factor of wind energy resources exceeds 0.4, indicating a distribution of high-quality wind energy resources (Liang et al 2021).
This study focuses on the period from 2006 to 2019 and examines carbon emissions in rural areas of five provinces in northwest China (Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang).It quantifies carbon emissions from various sources including household energy use, agricultural machinery, solid waste disposal, planting activities, and animal husbandry based on collected statistical data.The primary types of energy utilized in rural areas encompass raw coal, washed coal, gasoline, kerosene diesel fuel, liquefied petroleum gas (LPG), natural gas, and electricity.Data sources for this study primarily comprise the China Energy Statistical Yearbook, China Rural Statistical Yearbook, and the IPCC website (https://www.ipcc.ch/).Additionally, this study also investigates greenhouse gas emissions reduction resulting from new energy power generation in rural areas of these five provinces during the period between 2013 to 2019.Solar and wind energy are considered as alternative energy sources for this analysis.Data sources for this aspect of the research include China Energy Statistical Yearbook 2013-2019 and Carbon Emission Trading Website (http://www.tanpaifang.com/).

Rural emissions accounting
The baseline method outlined in the 2006 IPCC National Greenhouse Gas Inventory Guidelines (Eggleston et al 2006) is employed in this study, which is integrated with the emission factor method to accurately estimate carbon emissions from rural household energy consumption across various energy sources.
In the formula, C E refers to rural household energy carbon emissions or agricultural machinery carbon emissions (Gt ); i refers to the type of energy; AX i refers to the physical consumption of energy (10 4 tons, 10 8 m 3 or 10 8 kW h); NCVi refers to the lower heating value of each energy source (kJ/kg or kJ/m 3 ), and the lower heating value comes from the China Energy Statistical Yearbook; CC i refers to the carbon content of each energy source (kg/GJ), and the carbon content comes from IPCC reference values (table 1); O i refers to the oxidation rate, using the IPCC default value of 100%, which is considered complete combustion; 44/12 is the coefficient for converting C to CO 2 .Among them, the calculation of electricity carbon emissions is the electricity usage multiplied by the carbon emission factor.The carbon emission factor was taken from China's Chinese power grid's provincial carbon emission factor in 2018 (table 2).The accounting of carbon emissions from agricultural machinery, solid waste, planting, and animal husbandry also uses the baseline method for accounting.

Calculation of carbon emission reduction from new energy sources
The reduction in carbon emissions resulting from new energy generation is calculated by multiplying the solar and wind power generation with the regional baseline electricity carbon emission factors.The regional baseline carbon emission factor signifies the quantity of greenhouse gas emissions mitigated for each kilowatt-hour of electricity generated by new energy power facilities.
In the formula, C j refers to the carbon emissions reduced by new energy power generation (t); EP i refers to the power generation of the i th new energy source (MW h); OM refers to the regional baseline factor of electricity carbon emission (tCO 2 /MW h), and the data comes from the carbon emission trading website (table 3).

Decomposition of carbon emission driving factors
In the selection of research methods for identifying driving factors of carbon dioxide emissions, decomposition analysis models are widely employed.Among these models, structural decomposition analysis (SDA) and index decomposition analysis (IDA) are commonly utilized.IDA is particularly effective in analyzing the structural effects of rural residents' carbon emissions across five aspects: energy consumption, agricultural machinery, solid waste, planting, and aquaculture.However, IDA methods also encompass the Laspeyres index method and Divisia index method.The LMDI method within the Divisia index approach is extensively implemented due to its lack of residuals and ability to address zero-value issues.The LMDI method is widely used for determining influencing factors of carbon emissions as it offers objectivity not attainable by other methods.Therefore, this study adopts the LMDI method to decompose the driving factors of carbon emissions.Initially, Kaya identity is employed to decompose carbon emissions into four components: energy structure intensity effect, energy consumption intensity effect, economic effect, and population effect.The calculation formula for this decomposition model is: In the formula, CO , 2 E, GDP, and POP, respectively, refer to the total carbon emissions, total energy consumption, gross domestic product, and total population in the rural areas of northwest China; C , i E , i G , i and P i respectively refer to the carbon intensity of energy effect, energy consumption intensity effect, economic effect, and population effect.Formula (8) is decomposed via the LMDI decomposition method, where C t and C 0 are carbon dioxide emissions for the reporting and base periods.The decomposition result is:

Spatiotemporal variation in rural carbon emission levels and growth rates
Since 2006, the economy in northwest China has undergone significant growth, resulting in enhancements to living standards and an increase in energy consumption within rural areas.Notably, there are discernible disparities in the rates of growth for both total and per capita carbon emissions among provinces.From 2006 to 2019, there was a consistent upward trend observed in the average annual carbon emissions within rural areas across the five provinces of northwest China.Each province exhibited distinct variations in both total carbon emissions and growth rates; however, those provinces with higher emission levels also demonstrated higher growth rates (figure 2).
From 2006 to 2019, the average annual total carbon emissions in rural areas of the five provinces in northwest China increased from 60.15Mt to 83.49Mt, exhibiting an average annual compound growth rate of 2.55%.The yearly emissions averaged at 60.15Mt during the period from 2006 to 2010, escalated to 72.66Mt between 2011 and 2015, and further surged, reaching a peak of 83.19Mt from 2016 to 2019.Throughout this duration, the mean annual carbon emissions in rural areas of Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang provinces respectively fell within the ranges of 14. 22 ∼ 22.15 Mt,17.18 ∼ 19.26 Mt,5.81 ∼ 6.95 Mt,4.31 ∼ 4.53 Mt,and 18.04 ∼ 30.30Mt.Xinjiang exhibited the highest average annual carbon emissions in rural areas, followed by Shaanxi and Gansu.Due to Qinghai and Ningxia having only 20%-40% of the rural population compared with other provinces, the average annual carbon emissions in their respective rural areas were relatively lower.
From 2006 to 2008, the economies of the northwestern provinces experienced rapid growth; however, rural carbon emissions did not undergo significant changes.This phenomenon can be primarily attributed to the substitution of non-commercial energy sources with commercial ones, leading to a substantial reduction in usage and notable enhancement in energy utilization efficiency (Zhang and Yang 2009).Between 2006 and 2019, there were considerable alterations in total rural carbon emissions in Shaanxi and Xinjiang, with increases of 78.49% and 58.65%, respectively.In contrast, rural carbon emissions remained relatively stable in Gansu, Qinghai, and Ningxia.Specifically for Shaanxi province, a sudden surge occurred in total carbon emissions during the period of 2008-2009 at a growth rate of 29.38%, followed by a deceleration after 2011.Prior to 2014, Xinjiang's total carbon emissions remained steady; however, from 2011 to 2018 there was a relatively high average annual growth rate of total carbon emissions at approximately 9.47%.
As the economy and society continue to rapidly advance, there is an increasing demand for energy in rural areas of northwest China, resulting in a significant rise in carbon emissions.However, due to the slower pace of modernization and low energy utilization efficiency in this region, there remains a lack of introduction and application of carbon reduction technologies.Consequently, rural carbon emissions in the area are still growing continuously, albeit at a significantly slower rate.

Average annual carbon emissions and average annual growth rate in rural areas of northwestern China
Since 2006, the per capita carbon emissions in rural areas of northwest China have consistently increased and are approaching those of urban residents.In 2012, rural residents' per capita carbon emissions were only 10.12% lower than their urban counterparts (Qu et al 2013).The northwestern region experiences cold winters, and due to the absence of centralized heating systems in rural areas, energy consumption primarily relies on direct combustion of fossil fuels such as coal.As a result, per capita energy consumption significantly exceeds that of southern regions and northern urban areas.Rural residents enjoy improved living conditions, leading to escalating energy demands and a thriving economy.Before 2012, rural energy consumption was predominantly reliant on fossil fuels, and both energy consumption and per capita carbon emissions exhibited a synchronous growth trajectory.Subsequently, with the acceleration of China's urbanization process, certain provinces experienced a decline in their rural population.For instance, from 2012 to 2019, Gansu Province witnessed a notable decrease of 13.68% in its rural population.In 2018, there was a significant inflection point observed in per capita carbon emissions within the rural areas across all northwest Chinese provinces.Each province began displaying a relatively stable or declining trend in terms of per capita carbon emissions.This phenomenon can be attributed to the population decline as well as the rapid transformation of the energy structure wherein clean electricity has been increasingly replacing direct combustion of fossil fuels.Notably, in 2018 alone, the northwest power grid facilitated clean energy substitution by trading thermal power generation rights amounting to an impressive total of 21.2 billion kWh.The accelerated adoption of clean energy sources will effectively contribute towards mitigating greenhouse gas emissions.
From 2006 to 2019, per capita carbon emissions in each province exhibited a continuous upward trend, with the exception of Xinjiang.However, after 2009, Xinjiang also witnessed a significant surge in per capita carbon emissions (figure 3).A distinct disparity is observed between provinces with high overall emissions and those with elevated per capita carbon emissions.Our study unveils that provinces with lower overall emissions paradoxically exhibit higher levels of carbon emission on an individual basis.This phenomenon can be attributed to their comparatively slower pace of social and technological development, reliance on inefficient energy sources, and lower population density.
The per capita carbon intensity of Xinjiang experienced a decline from 2006 to 2008, followed by an increase after 2009.Conversely, the other four provinces witnessed a continuous growth in carbon intensity.This suggests that there will be a significant future increase in carbon emissions among rural residents.Despite the declining rural population resulting from urbanization in China, the escalating carbon intensity of these residents renders their emissions indispensable.
Surprisingly, despite the fact that Shaanxi's total rural carbon emissions are 2.20 ∼ 4.69 times higher than those of Ningxia or Qinghai, the per capita carbon emissions in Ningxia and Qinghai are respectively 1.12 ∼ 2.40 and 1.90 ∼ 3.24 times greater than those in Shaanxi.Moreover, Xinjiang also falls within a region characterized by high per capita carbon emissions.This result is closely associated with disparities in regional economic structure and levels of technological development among these areas.In comparison to Qinghai, Ningxia, and Xinjiang, Shaanxi has a smaller scale of animal husbandry operations, higher population density, improved energy utilization efficiency, as well as more widespread implementation of carbon reduction technologies and practices.Additionally, the relatively low winter temperatures experienced in Qinghai, Ningxia, and Xinjiang lead residents to consume significant amounts of energy for heating purposes during this season which consequently results in elevated per capita carbon emissions.

Analysis of the contribution of rural residents
The primary factors contributing to carbon emissions in rural areas of northwest China include residential activities, the planting industry, and animal husbandry (figure 4).Residential energy consumption emerges as the key driver for changes in total carbon emissions in rural Shaanxi.Conversely, the provinces of Gansu, Qinghai, Ningxia, and Xinjiang exhibit significant agricultural and animal husbandry sectors, leading to higher carbon emissions from economic output industries.
The primary factor contributing to carbon emissions in the rural areas of the northwest region is residential sources.As a result of China's policy on rural revitalization, there has been a significant enhancement in the quality of life in these regions, leading to an increase in energy consumption and subsequent carbon emissions.From 2006 to 2019, the proportion of carbon emissions from residential sources exhibited a consistent upward trend, rising from 28.12% to 45.64%.Shaanxi Province, characterized by its high population density and living standards in rural areas, exhibits the highest proportion of carbon emissions resulting from residential energy use; this proportion exceeded 50% after 2010.However, among the five provinces examined, Ningxia experienced a decline in this ratio due to substantial population loss; by 2019, Ningxia's rural population had decreased by 32% compared to that in 2006.The carbon emissions resulting from planting and animal husbandry activities in the northwest region constitute approximately 50% of the total carbon emissions in rural areas.The rapid expansion of these two industries has led to a substantial increase in carbon emissions, serving as a significant catalyst for regional carbon emission growth.Gansu, Qinghai, Ningxia, and Xinjiang provinces exhibit similar patterns in their carbon emission structures, characterized by a high proportion of emissions originating from planting and animal husbandry practices.In 2019, the cultivated areas for the agricultural sector were recorded at 38316 km 2 , 5535 km 2 , 11530 km 2 and 61700 km 2 respectively for Gansu Province, Qinghai Province, Ningxia Hui Autonomous Region, and Xinjiang province; while livestock production reached figures of 65.869 million heads, 15.495 million heads, 24.721 million heads, and 142.911 million heads respectively.The contribution of planting and animal husbandry to overall carbon emissions ranged between 45% to 64%.Economic output emerged as the primary driving force behind rural carbon emissions across these four provinces.
Additionally, approximately 10%-20% of carbon emissions come from solid waste and agricultural machinery.The northwest region has experienced a rapid expansion in agricultural mechanization since the turn of the century, with total power capacity reaching 84.11 GW by 2019 -representing 8.2% of China's overall agricultural machinery capacity.This widespread use of agricultural machinery is a significant contributor to increasing rural carbon emissions; however, there is an emerging trend towards electrification in agriculture machinery that may lead to substantial reductions in carbon emissions over the next decade.Furthermore, efforts to reduce solid waste generation at its source and implement centralized treatment, recycling, and resource utilization methods have yielded significant results in reducing rural solid waste carbon emissions in the northwest.

Rural carbon emission structure in the five northwestern provinces
Northwest China possesses abundant resources of photovoltaic and wind energy.By 2019, the installed capacity for wind power had reached 52.7 GW, while photovoltaics had an installed capacity of 47.8 GW (Zhou et al 2020).Moreover, ongoing rapid construction of new energy power generation facilities in the region has led to significant growth in both installed capacity and grid-connected electricity, resulting in profound changes to the regional energy structure.Given their inherent characteristics of centralized distribution, photovoltaics and wind power are well-suited for installation in rural areas with vast land and sparse population.This large-scale substitution of energy sources has effectively reduced carbon emissions within rural areas at a remarkable pace.To quantify the carbon emission reduction effect from new energy power generation across various provinces in northwest China, this study combines it with local conventional carbon emissions data and analyzes the changes in total rural carbon emissions within different provinces from 2013 to 2019 (figure 5).
From 2013 to 2019, the northwest region experienced a continuous growth in overall carbon emission reduction from new energy sources.Ningxia, Qinghai, Gansu, and Xinjiang achieved carbon neutrality in their rural areas in 2013, 2015, 2016, and 2017 respectively.Although Shaanxi did not achieve carbon neutrality during this period, its rural carbon emissions decreased by 64.18%, indicating a significant impact from the reduction of new energy-related emissions.Shaanxi Province has high levels of rural carbon emissions and limited access to clean resources such as solar and wind energy, resulting in lower levels of carbon emission reduction.On the other hand, Ningxia has relatively low levels of carbon emissions and abundant clean resources like solar and wind energy which enabled it to achieve zero-carbon emissions in its rural areas as early as 2013.

Analysis of carbon emission driving factors in the northwestern region
The economic development significantly contributes to rural carbon emissions in the northwest region, although the marginal effect is starting to emerge.The effects of energy structure intensity, energy consumption intensity, and population exhibit varying degrees of inhibition on rural carbon emissions in the northwest region (table 4).
The primary driving factor for rural carbon emissions in the northwest region is the economic effect.From 2010 to 2019, the economy of the northwest region experienced rapid development, with GDP growth surpassing the national average.Rural carbon emissions are closely intertwined with economic development.The swift progress of the economy has resulted in a substantial surge in rural carbon emissions in the northwest region.These findings have been corroborated by scholars (Feng et al 2013, Liu et al 2022a, 2022b).However, as both the economy and society continue to advance, the promoting influence of economic effects gradually diminishes.
The inhibitory effects on rural carbon emissions in the northwest region are attributed to the optimized energy structure and reduced consumption intensity during this period.This can be explained by the decreased use of inefficient non-commercial energy sources and the increased utilization of commercial energy, particularly electricity.Furthermore, with the implementation of the dual carbon plan, there has been a rapid rise in the proportion of clean energy.The impact of energy consumption intensity serves as an indicator for assessing energy efficiency levels.Enhancing energy efficiency implies more optimal utilization of energy resources, thus strengthening the effect of energy consumption intensity is crucial for effectively curbing carbon emissions.With the progress of urbanization, there is a continuous decline in the rural population in the northwest region, which has a restraining effect on rural carbon emissions.Residential energy consumption plays a significant role in contributing to these emissions.The decrease in population will result in a substantial reduction in energy usage and consequently inhibit rural carbon emissions.However, as social development improves living standards for rural residents, per capita carbon emissions are also increasing.In the context of this ongoing growth in per capita carbon emissions, the declining population in the northwest region has a slight inhibitory impact on rural carbon emissions that is expected to remain relatively stable over the next decade.This conclusion was supported by decomposed research analyzing driving factors of carbon emissions at global, national, and provincial scales (Wang et al 2017, Ma et al 2019, Duan et al 2022).

Conclusion and discussion
By conducting a comprehensive analysis of carbon emissions in rural areas of northwest China spanning from 2006 to 2019, this study establishes a robust dataset that serves as a foundation for the development of targeted policies aimed at mitigating carbon emissions in the region.
From 2006 to 2019, the total carbon emissions in rural areas of northwest China increased from 60.15Mt to 83.49Mt, exhibiting a consistent annual growth rate of 2.55%.The primary drivers behind this trend are residents' livelihoods, agricultural practices, and aquaculture activities.Specifically, Shaanxi province is predominantly influenced by carbon emissions from livelihoods, while Gansu, Qinghai, Ningxia, and Xinjiang provinces are primarily impacted by aquaculture and planting-related emissions.Moreover, as living standards improve and agricultural mechanization advances across these regions, per capita carbon emissions in rural areas have surged from 0.93t to 1.78t at an impressive growth rate of 90.14%.Notably higher per capita carbon emissions were observed in Qinghai and Ningxia compared to Shaanxi and Xinjiang provinces respectively.Economic development has played a significant role in driving up carbon emissions; however, factors such as energy structure intensity effectivity, energy consumption intensity effectivity, and population dynamics have acted as inhibitors.
The utilization of new energy sources has emerged as a pivotal determinant of carbon emissions in northwest China.Despite witnessing a rapid increase in greenhouse gas emissions, the widespread adoption of renewable electricity has disrupted the continuous growth trajectory of carbon emissions.By 2019, the emission reductions achieved through new energy power generation in rural areas had surpassed the total carbon emissions within this region, with this disparity continuing to expand at an accelerated pace.
The five provinces in the northwest should expedite the restructuring of the agricultural industry while ensuring food security.Simultaneously, they should decrease cultivation of high-input and high-consumption crops, foster new agricultural varieties, and enhance cultivation of high-yield and efficient N 2 O-absorbing crops.Per capita carbon emissions in Qinghai and Ningxia are significantly higher than those in Shaanxi and Xinjiang; therefore, their local governments ought to promptly promote social and technological development, introduce advanced energy utilization technologies, implement low-carbon energy-saving techniques, and strengthen effective allocation as well as intensive utilization of energy resources.By prioritizing the development of new energy power alongside energy-saving technological innovation, upgrading industrial structure, adjusting energy consumption structure among other measures may serve as a crucial driver for decoupling carbon emissions from economic strength in northwest China.
Due to the substantial disparity in carbon emissions between urban and rural areas, conventional quantitative analysis alone proved inadequate in accurately capturing the characteristics of rural carbon emissions.Against the backdrop of global climate change, northwest China's environment is increasingly fragile, plagued by frequent environmental issues.By examining the structure and driving factors behind carbon emissions in this region, this study portrays the trajectory of carbon emissions under the influence of new energy emission reduction measures, thereby offering valuable insights for implementing strategies aimed at reducing rural carbon emissions in northwest China.

Figure 1 .
Figure 1.Geographical location of northwest China.

Figure 2 .
Figure 2. Average annual carbon emissions and average annual growth rate in rural areas of northwestern China from 2006 to 2019.

Figure 3 .
Figure 3. Annual and per capita carbon emissions of rural areas in five northwest provinces from 2006 to 2019.

Figure 4 .
Figure 4.The structure of rural carbon emission in the five provinces in northwest China.

Table 1 .
The lower heating value and carbon content for each energy source.

Table 2 .
Provincial electricity carbon emission factors.

Table 3 .
Regional baseline factor of electricity carbon emission.

Table 4 .
Results of LMDI model decomposition of carbon driving factors.