甘南高原不同生计方式农户的碳足迹分析

PDF HTML阅读 XML下载 导出引用 引用提醒 高寒生态脆弱区农户对气候变化的感知——以甘南高原为例 DOI: 10.5846/stxb201507271565 作者: 作者单位: 西北师范大学,西北师范大学,西北师范大学,西北师范大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41361106，41471116）；教育部新世纪优秀人才支持计划（NECT-11-0910）；甘肃省高校基本科研业务费项目 Farmers' perception of climate change in high-frigid ecological vulnerable region: a case of Gannan Plateau Author: Affiliation: Northwest Normal University,Northwest Normal University,Northwest Normal University,Northwest Normal University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:气候变化作为人类当前面临的最严峻挑战，已对生态脆弱区农户生计产生严重的负面影响，明确农户对气候变化的感知对于制定有效的气候变化适应政策非常关键。以甘南高原为研究区，基于入户调查数据，构建了农户对气候变化的感知度指数，分析了甘南高原农户的气候变化感知特征，并采用经济计量模型分析了影响农户气候变化感知的关键因素。结果表明：（1）甘南高原农户对气温变化的感知能力强于对降水变化的感知，并对近期发生的、规模较大、影响较严重的极端天气记忆较深；（2）农户对气候变化的严重性及可能性感知较强烈，感知到的适应成本与适应功效也较高，但感知到的自我效能较弱，其气候变化严重性、可能性、适应功效、自我效能及适应成本感知度指数分别为3.76、3.34、3.43、2.85、3.53，且农区农户对气候变化的风险感知与适应感知均最强，半农半牧区次之，纯牧区最弱；（3）气候变化信息、农户的客观适应能力、农户对社会话语的信任度、适应激励均会影响农户的气候变化感知，其中，适应激励为最关键的影响因素，其与农户的气候变化适应功效感知、自我效能感知均呈正相关，而与风险感知、适应成本感知呈负相关。最后，针对如何提高农户气候变化感知的准确度，增强农户应对气候变化的能力，提出相关的政策建议。 Abstract:Global climate change has increased the vulnerability of natural ecosystems and brought a huge challenge to human being, how to adapt to climate change has become a hot spot in the world. The farmers who rely on natural resources for livelihoods is one of the vulnerable groups to adapt to climate change. Climate change has a serious negative impact on the farmers' livelihood, especially in the high-frigid ecologically vulnerable region. Perception of climate change is the basis of climate change adaptation, clearing the features of climate change perception of farmers is conducive to a better understanding of their response action. Selecting Gannan Plateau as the study area, based on 539 household survey data and the MPPACC (Model of Private Proactive Adaptation to Climate Change), built a index of the farmers' perception of climate change, this paper analyzes the features of farmers' perception to climate change and the key factors that affect the farmers' perception of climate change by using the econometric model. The results show that:(1) Farmers' perception of temperature change is more accurate than the perception of precipitation change,79.96% of farmers have sensed accurately the trend of rising temperatures over the past 30 years, which is consistent with the meteorological data, and only 36.92% of farmers have sensed the trend of rising precipitation, which is inconsistent with the actual meteorological data; At the same time, farmers have a deep memory of the influential and larger scale extreme weather that happened in recent years;(2) Farmers' severity perception, probability perception, adaptation efficacy perception and adaptation costs perception of climate change are strong, but the self-efficiency perception of climate change is weak, the index of their severity perception, probability perception, adaptation efficacy perception, self-efficacy perception and adaptation cost perception are 3.76, 3.34, 3.43, 2.85, 3.53 respectively. And these perception is the strongest in farming area, next in the planting-pasturing area, the pure pasturing area is the weakest.(3) Climate change information, farmer's objectively adaptive capacity, farmers' trust on the social discourse, and adaptation incentives are all affect the farmer's perception of climate change, among them, adaptation incentive is the key factors influence farmers' climate change perception, and the more adaptation incentives, the weaker farmers' severity perception, probability perception and adaptation costs perception, but the stronger farmers' adaptation efficacy perception and self-efficiency perception. Finally, we put forward some relevant policy recommendations, the purpose is improve the accuracy of farmers' perception to climate change and enhance their adaptive capacity. 参考文献 相似文献 引证文献

Tourism is sensitive to global climate change and greenhouse gas emissions generated by the energy consumption restrict the sustainable development of tourism industry. Methods trying to make the tourism development of sustainability measurable are too confused to be of practical value. To overcome this obstacle, this paper built a Tourism Carbon Footprint Model based on energy consumption structure. With this TCF model, we analyzed carbon footprint of Huangshan between 2006 and 2009. We find that: (1) total carbon footprint of Huangshan increases from 2674.92 hm2 to 3456.76 hm2 at the annual rate of 7%; (2) energy consumption carbon footprint differs significantly in structure. The five kinds of energy carbon footprint, in the descending order, are the electricity, coal, diesel, gasoline, liquefied petroleum gas; (3) analysis of energy consumption carbon footprint shows that Huangshan is sustainable between 2006 and 2009.

We welcome the correspondence from O'Sullivan and Shah [1], which comments on our study [2]. I agree broadly with their points and wish to emphasise that our results should not be interpreted as a minimum carbon footprint for total intravenous anaesthesia (TIVA). A common concern about TIVA is the high consumption of single-use plastic compared with volatile inhalational anaesthesia [3]. Anaesthetists might conclude that TIVA has a greater carbon footprint than sevoflurane anaesthesia and our model assumed ‘worst case’ use of consumables for each of our TIVA scenarios. We included all possible consumables that were used so that our calculations of the total carbon footprint of TIVA overestimated actual practices. By using this approach, and by publishing the carbon footprints of individual items used in our scenarios, clinicians can identify how the footprint of their practice might vary from the scenarios we modelled, as O'Sullivan and Shah have done. In our modelling, we assumed that all TIVA cases use processed electroencephalogram (pEEG), as many anaesthetists, particularly during training, choose to use this to minimise the risk of accidental awareness even in the absence of neuromuscular blockade [4]. As discussed in our study, if pEEG is not used for TIVA, the carbon footprint will reduce by 0.83 kgCO2e (excluding the energy required for the pEEG monitor), equating up to 28.1% of the total carbon footprint of TIVA. In this case, after 11 min, both TIVA scenarios have lower total carbon footprints than median consumption inhalational anaesthesia using sevoflurane. Conversely, if pEEG is also used for volatile anaesthesia to reduce recovery time and volatile anaesthetic agent consumption as highlighted by O'Sullivan and Shah [1] and to reduce accidental awareness [4], then the total carbon footprint of volatile sevoflurane anaesthesia will increase by at least 0.83 kgCO2e. Using our modelled scenarios, if pEEG is used for both TIVA and sevoflurane anaesthesia, then, after 20 min, the highest emitting TIVA scenario has a lower total carbon footprint than the most economical sevoflurane scenario. This 20-min threshold is drastically lower than the 44 min for our original high-emission TIVA scenario vs. minimal consumption sevoflurane without pEEG. It should be noted that data used for sevoflurane consumption were derived from a study that did not use pEEG to titrate sevoflurane but instead measured actual consumption using low fresh gas flow [5]. O'Sullivan and Shah highlight several TIVA practices that use resources more efficiently than our modelled scenarios and we encourage anaesthetists to consider how their practices can be leaner and greener while maintaining patient safety.

In the Netherlands, central softening of drinking water is widely applied for reasons of public health, client comfort, economic and environmental benefits. Currently, the detrimental contributions of softening, in particular the use of chemicals and energy, are taken into account in the carbon footprint of the Dutch drinking water companies. The beneficial contributions have, however, until now not been included in the carbon footprint. Here, we present an attributional life cycle assessment (LCA) for the softening of drinking water, including effects at the household level and several sensitivity analyses. Five central softening methods were included (pellet reactor (PR), water storage reservoir (WSR), reverse osmosis (RO), nanofiltration (NF) and ion exchange (IEX)). Domestic softening by IEX is represented as well. Central softening, except RO, is shown to reduce the carbon footprint when effects at the household level are included. The main detrimental contributions are caused by the consumption of chemicals and energy in the softening process. The main beneficial contributions of softened water with respect to the carbon footprint are at the household level. Decreases in water hardness result in reduced scaling and give rise to a prolonged lifespan of appliances in which water is heated, reduced energy use of those appliances and less required cleaning agents. For PR and WSR a new beneficial effect was identified; carbon capture in the crystallized calcite and dissolution of CO2 into the softened water. We show that for the Dutch water companies Evides and Waternet approximately 20% and 60%, respectively, of their total carbon footprint is compensated by the net carbon benefit of softening. The net total carbon footprint of drinking water softening in the Netherlands is estimated to be −0.11 Mtonne CO2 eq./yr.

With over 1 billion passenger cars worldwide, even an indiscernible carbon footprint (CF) of the maintenance of a single vehicle has the potential for a relatively large overall environmental effect on the part of the car washing services sector. This study presents a methodology for assessing the CF of car washing services with a focus on Polish conditions. It compares four types of professional car washes, i.e., jet, rollover, tunnel, and hand, analyzing their environmental impact based on CO2 emissions from water heating systems, energy consumption, water supply, and wastewater treatment. The results reveal that the total CF per vehicle ranges from 0.88 kg CO2 (hand wash with gas water heating) to 4.46 kg CO2 (rollover wash with electric heating). The key contributors to CF are electricity usage and the water heating system applied. Annual CO2 emissions from car washing services in Poland are estimated at between 373.5 and 709.4 Gg, representing 0.13–0.26% of the country’s total net CO2 emissions. Transitioning entirely to hand washes could reduce the emissions of the whole sector by approximately 50%. This study highlights the environmental significance of car washing services and provides practical insights for consumers and service providers.

Deep fertilization is a beneficial approach for reducing nitrogen losses. However, the effects of various fertilization depths on maize (Zea mays L.) productivity and environmental footprints have not been thoroughly understood. Therefore, a field experiment was conducted to investigate the effects of different fertilization depths of 5 cm (D5), 15 cm (D15), 25 cm (D25), and 35 cm (D35) on maize productivity and environmental footprints. Reactive nitrogen (Nr) losses and greenhouse gas (GHG) emissions were assessed using life cycle analysis. We hypothesized that deep fertilization can obtain lower carbon and nitrogen footprint. The results indicated that deep fertilization decreased the N2O and NH3 emissions while increasing the CH4 uptake. Compared with D5, D15 resulted in an increase in total GHG emissions and carbon footprint (CF), whereas D25 decreased by 13.0% and 23.6%, respectively. Compared with D5, the Nr losses under D15, D25, and D35 conditions was reduced by 11.3%, 17.3%, and 21.0%, respectively, and the nitrogen footprint (NF) was reduced by 16.0%, 27.4%, and 19.0%, respectively. The maize yield under D15 and D25 increased by 5.7% and 13.8%, respectively, compared with the D5 treatment, and the net economic benefits of the ecosystem increased by 7.1% and 17.1%, respectively. In summary, applying fertilizer at a depth of 25 cm can significantly reduce the environmental footprints and increase maize productivity, making it an effective fertilization strategy in the Loess Plateau region of China.

Current study intends to find out the carbon footprint of Dokuz Eylül University's Tınaztepe Campus and comprehensively assess its environmental impact in the process. The study provides a detailed analysis of carbon emissions resulting from the campus’s energy consumption, transportation, water usage, and other sources. Based on these analyses, the total CO2 emmission of the campus has been determined, taking into account the carbon sequestration capacity of the university's forested area. Calculations performed using the IPCC Tier 1 Model estimate that Tınaztepe Campus's total annual carbon footprint is 2,458.44 tons of CO2. Additionally, the annual carbon footprint per capita has been calculated as 0.059 tons of carbon footprint. According to the findings, the largest portion of carbon emissions is from natural gas consumption, while the smallest is from water consumption. In light of this data, various strategic recommendations have been developed to reduce the campus’s carbon footprint. These recommendations include measures such as increasing energy efficiency, adopting more sustainable transportation methods, and reducing water consumption. The results of this study provide valuable insights for universities to consider when developing sustainability policies.

China is the largest carbon emitter in the world; thus, reducing carbon emissions while maintaining economic growth has become an important issue. Within the context of carbon neutrality strategies, calculation of the carbon footprint and embodied carbon transfer can help policymakers formulate reasonable carbon reduction plans. The multi–regional input–output (MRIO) model can clarify carbon flow pathways between regions, and social network analysis (SNA) can comprehensively evaluate the different positions of individual sectors. Combining these two approaches, the specific characteristics of carbon emissions in complex production and trade relationships can be analyzed. China has become the world’s top total carbon emitter, and the Hanjiang River basin (HJRB) constitutes an important economic link between the developed and less developed regions of China. Studying carbon emissions in the HJRB can provide a reference for other, similar regions and is vital for the realization of China’s carbon emission reduction targets. This paper examines the carbon footprint and embodied carbon emission transfer among three provinces and 12 sectors in the HJRB during different periods and identifies the key industries in the carbon transfer process. The results indicate that (1) the total carbon footprint in the HJRB exhibits an increasing trend. Energy-based Shaanxi Province exhibits the highest growth rate of the carbon footprint, agriculture-based Henan Province shows a decreasing trend, and consumption-based Hubei Province displays the lowest carbon footprint intensity. (2) There are differences in the carbon emission coefficient and final consumption rate among various sectors; construction, metal processing and metal and non-metallic products, processing and manufacturing of petroleum, coking, nuclear fuel, chemical products, and other services are the sectors accounting for a high proportion of emissions. (3) The more obvious the supply relationship is, the higher the flow of embodied carbon emission transfer between sectors. (4) Energy-based regions transfer large amounts of fossil energy, electricity, steel and coal resources to developed regions and simultaneously assume more of the carbon reduction pressure imposed on developed regions. (5) The key industries within the embodied carbon emission transfer network notably control the carbon emissions of other industries and can provide breakthroughs to achieve challenging carbon emission reduction targets.

As stated in the 2016 Paris Agreement, concerns about global climate change and carbon emissions have increased, and organizations, in particular, have embarked on an annual measurement process to estimate their contribution to global climate change. Carbon footprint, one of the measurement methods, is a widely applied tool to assess the environmental impact of organizations. This study presents a real case study of a denim-washing company’s activities based on ISO standard calculation methods of greenhouse gas emissions. Accordingly, the annual carbon footprint of the denim-washing company was 2482.09 tCO2e for the year 2021 in total for the overall carbon footprint. Direct emission was calculated at 1575.75 tCO2e, indirect energy–related emission at 798.09 tCO2e, and indirect non-energy–related emission at 108.25 tCO2e. The highest CO2 emissions are related to heating from greenhouse gas direct emission sources, followed by purchased electricity consumption, and the lowest CO2 emissions are related to fire–CO2 tube storage. In conclusion, this study is particular in that it analyzes not only the specific processes of a denim-washing company but also the overall organizational carbon footprint calculation, assesses the importance of indirect non-energy in the total carbon footprint, and evaluates the calculation findings with sector-specific mitigation strategies.

As the population increases, the growing demand for residential housing escalates construction activities, significantly impacting global warming by contributing 42% of primary energy use and 39% of global greenhouse gas (GHG) emissions. This study addresses a gap in research on lifecycle assessment (LCA) for Indian residential buildings by evaluating the full cradle-to-grave carbon footprint of a typical single-family house in Northern India. A BIM-based LCA framework was applied to a 110 m2 single-family dwelling over a 60-year life span. Operational use performance and climate analysis was evaluated via cove tool. The total carbon footprint over a 60-year lifespan was approximately 5884 kg CO2e, with operational energy use accounting for about 87% and embodied carbon approximately 11%. Additional impacts came from maintenance and replacements. Energy usage was calculated as 71.76 kWh/m2/year and water usage as 232.2 m3/year. Energy consumption was the biggest driver of emissions, but substantial impacts also stemmed from material production. Cement-based components and steel were the largest embodied carbon contributors. Under the business-as-usual (BAU) scenario, the operational emissions reach approximately 668,000 kg CO2e with HVAC and 482,000 kg CO2e without HVAC. The findings highlight the necessity of integrating embodied carbon considerations alongside operational energy efficiency in India’s building codes, emphasizing reductions in energy consumption and the adoption of low-carbon materials to mitigate the environmental impact of residential buildings. Future work should focus on the dynamic modeling of electricity decarbonization, improved regional datasets, and scenario-based LCA to better support India’s transition to net-zero emissions by 2070.

Reducing the impact of human activities on the environment requires prioritizing awareness. Measuring the direct and indirect effects of human actions is crucial to increasing awareness and prompting behavioral change. The carbon footprint is a widely used method of measurement in this regard. This study comprises three parts. The first part involved providing pre-training to students and teachers at Lokman Hekim Vocational and Technical Anatolian High School, which has 304 students and 29 teachers. A 15-question awareness questionnaire was then administered to determine their understanding of carbon footprints. The results showed that the students were knowledgeable about climate change and global warming but lacked sufficient understanding of carbon footprints. In the second part of the study, a face-to-face measurement study was conducted with 295 students to calculate the annual average amount of CO2 emitted by the students based on categories. The results showed that the students emitted an average of 7.43 tons of CO2 per year. The highest amount of emissions, 3 tons CO2e/year, was attributed to household factors, while 2.135 tons CO2e/year resulted from lifestyle habits. In the third part of the study, the institutional carbon emissions released by the high school building were calculated. The emission amounts from Scope 1, Scope 2, and Scope 3 were determined to be 85,368, 16,716, and 18,916 tons CO2e/year, respectively. As a result, the total institutional carbon footprint of the high school was calculated to be 120,984 tons of CO2e/year.

In order to achieve target greenhouse gas (GHG) emissions, such as those proposed by each country by nationally determined contributions (NDCs), GHG emission projections are receiving attention around the world. Generally, integrated assessment models (IAMs) are used to estimate future GHG emissions considering both economic structure and final energy consumption. However, these models usually do not consider the entire supply chain, because of differences in the aims of application. In contrast, life cycle assessment (LCA) considers the entire supply chain but does not cover future environmental impacts. Therefore, this study aims to evaluate the national carbon footprint projection in Japan based on life cycle thinking and IAMs, using the advantages of each. A future input–output table was developed using the Asia-Pacific integrated model (AIM)/computable general equilibrium (CGE) model (Japan) developed by the National Institute for Environmental Studies (NIES). In this study, we collected the fundamental data using LCA databases and estimated future GHG emissions based on production-based and consumption-based approaches considering supply chains among industrial sectors. We targeted fiscal year (FY) 2030 because the Japanese government set a goal for GHG emissions in 2030 in its NDC report. Accordingly, we set three scenarios: FY2005 (business as usual (BAU)), FY2030 (BAU), and FY2030 (NDC). As a result, the carbon footprint (CFP) in FY2030 will be approximately 1097 megatons of carbon dioxide equivalent (MtCO2eq), which is 28.5% lower than in FY2005. The main driver of this reduction is a shift in energy use, such as the introduction of renewable energy. According to the results, the CFP from the consumption side, fuel combustion in the use stage, transport and postal services, and electricity influence the total CFP, while results of the production side showed the CFP of the energy and material sectors, such as iron and steel and transport, will have an impact on the total CFP. Moreover, carbon productivity will gradually increase and FY2030 (NDC) carbon productivity will be higher than the other two cases.

This study aimed to estimate the carbon footprint of Philippine households from consuming various goods and services. Data from the Philippine Input-Output Table and Global Trade Analysis Project’s carbon emission coefficients were used to extract the carbon intensities of different economic sectors. The embodied carbon emission from different consumption items was estimated by tracing the associated emission down to its intermediate inputs used in the production. The total household carbon footprint was derived by summing up the carbon emission from each consumption category. Results showed that the highest carbon emitting goods consumed by households are related to expenditure on fuel, light and transportation while nondurable and recreation goods were the least carbon intensive. Different socio-economic characteristics of the households matter in explaining total household carbon footprint. By using non-parametric estimation, results showed a strong positive relationship between household carbon footprint and income but the effect varies across the distribution. This implies that further increases in carbon footprint are to be expected as households get richer. Policy makers should devise policies promoting green consumption or low-carbon lifestyle; else it is likely that households will be leading a carbon intensive lifestyle as they become more affluent.

Carbon footprint involves the calculation of direct and/or indirect emissions of fossil fuels that emit greenhouse gases (GHG) that lead to greenhouse effect responsible of global warming. The resulting carbon dioxide (CO2) due to the activities of the individuals/institutions emerges into the atmosphere with the consumption of energy. The amount of emissions throughout the world in general and in Turkey, and the main reasons of these emissions is explained in this study. Carbon footprint management and tracking since 1990’s in Turkey are underlined, and carbon tax and carbon trade terminologies are introduced. Scope classification for determining emissions according to three classification of ISO 14064 Greenhouse Gas Calculation and Validation Management System is described. Scope-1 covers the activities that create direct carbon footprint. In this context, the fossil fuels used by the projects for heating or energy needs and the emissions from the fuels of the vehicles are taken into consideration. Within Scope-2, the carbon footprints of the emissions caused by the electrical energy consumed by the projects are considered. Scope-3 is an indirect carbon footprint and includes emissions from projects that are not directly emission-driven projects. With this study, it is aimed to address the carbon footprint caused by the entire construction sector that extends from the production of construction materials to the construction and post-construction (operation) stages. This sector is focused on due to its significance regarding GHG emissions globally. Emissions from non-owned or uncontrolled sources such as production, transportation, leased assets, outsourced services and disposal of the wastes generated during the construction or operation of the stages of different building typologies are included in the carbon footprint calculations.

As it is revealed in the literature, there is an intense energy consumption in “Selus Per Aqua” (SPA) centers. Especially electricity and natural gas used for heating, cooling and lighting have very serious negative environmental effects. In particular, meeting these energy sources from fossil fuels and supplying water consumption from clean water sources pose a threat to sustainable environmental targets. In this context, the aim of the research is to calculate the carbon footprint based on energy consumption in 20 SPA centers located in different cities in Turkey and Lithuania. Electricity, natural gas and water consumption amounts consumed during 2022 in 20 SPA centers located in different cities in Turkey and Lithuania were used as a data set. Using the calculation methodology (Tier 1 method) put forward by The Intergovernmental Panel on Climate Change, the carbon dioxide emission amount (carbon footprint) of the SPA facilities was calculated. While the annual total carbon footprint of 20 SPA centers in Turkey and Lithuania is calculated as 6.092805 kgCO2eq, the carbon footprint calculated for Turkey is 2.947006 kgCO2eq. The carbon footprint calculated for Lithuania is 3.145079 kgCO2eq. SPA centers, where health, tourism and recreation services are offered together, are centers where human mobility and energy use are inevitable due to their year-round use potential. Research findings suggest that SPA centers cause negative environmental consequences due to energy and water consumption. It is very important to prefer clean energy sources for sustainable environment, health, tourism and recreation and to avoid using clean water resources.