ارزیابی الگوی توزیع و پراکنش محرک‌های انتشار گازهای گلخانه‌ای (GHGs) در میان کشورهای جهان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم محیط زیست، دانشکده علوم، دانشگاه زنجان، زنجان، ایران

2 گروه علوم محیط زیست، دانشکده علوم، دانشگاه زنجان

چکیده

تغییر اقلیم به‌عنوان بزرگترین تهدید بشری، نیازمند اقدامات کنترلی سریع و شناسایی محرک‌های کاهش انتشار با کمترین پیامد اقتصادی است. پژوهش حاضر به ارزیابی مهم‌ترین عوامل موثر در انتشار گازهای گلخانه‌ای در کشورهای جهان (2012-1971) پرداخته است. جمعیت، سرانه تولید ناخالص داخلی، شدت انرژی و شدت کربن انتخاب و برای بررسی روابط فضایی انتشار از تکنیکهای خودهمبستگی فضایی موران جهانی و محلی استفاده شد. نتایج نشان داد همبستگی مثبت و رو به رشدی در انتشار کربن‌‌دی‌اکسید وجود دارد و اوج این همبستگی به زمان شکل‌گیری IPCC باز می‌گردد و رشد اقتصادی نزدیک‌ترین ارتباط را با انتشار دارد. براساس ارزیابی موران جهانی تمامی پارامترها دارای خودهمبستگی مثبت و پیرو توزیع خوشه‌ای هستند. تحلیل موران محلی نشان می‌دهد در دهه‌های ابتدایی خوشه‌های بالا-بالا در کشورهای صنعتی و با گذشت زمان کشورهای در حال توسعه را شامل شد. جمعیت آسیا، سرانه تولید ناخالص داخلی اروپا و آمریکا، شدت انرژی در آسیا و معدود کشورهای آفریقایی و شدت کربنی در آسیا و آمریکا مهم‌ترین عوامل تشدید انتشارها در جهان به‌شمار می‌روند. شناسایی محرک‌های انتشار موضعی و منطقه‌ای در تعیین سهمیه‌های انتشار و مسئولیت‌های کاهش انتشار نقش موثری دارد و می‌تواند معاهدات منطقه‌ای و مورد پذیرش کشورها را در پی داشته باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of the Distribution Pattern of Driving Forces of GHG Emissions among World Countries

نویسندگان [English]

  • Azadeh Tavakoli 1
  • Younes Khosravi 2
  • Mohammad Mehdi Doaiee 1
1 Department of Environmental Sciences, Faculty of Sciences, University of Zanjan, Zanjan, Iran
2 Environmental Science Faculty of Science. University of Zanjan
چکیده [English]

Introduction
Climate change and its consequences as an irreversible situation changed to the focal point for many researchers over the last two decades. These challenges are a result of the over-emission of greenhouse gases (GHGs). According to the 2018 World Rankings, China (28%), USA (15%), India (7%), Russia (5%), Japan (3%), Germany (2%), Iran (2%), Korea (2%), Saudi Arabia (2%) and Indonesia (2%) are the top ten emitters. The economic damages on one side and the uncertain future of the planet because of this phenomenon on the other side has led to many international efforts to converge the countries, put emission reduction strategies and limitations on carbon footprints.
Among the many meetings that have been held to reach a comprehensive international agreement, the COP21 Summit could be considered as the first global and legally binding agreement on climate change. The agreement was accepted and agreed on December 12, 2015, among 195 countries. The turning point of this agreement compared to previous efforts is that in this document not only 40 developed and industrialized countries, but also all countries, developed and developing, from 2020 are committed to reduce greenhouse gas emissions. Various approaches are proposed to reduce emissions, which are placed at different levels in terms of economics and public acceptance. Each country seeks to avoid emission reduction responsibilities and to act as cost-effective as possible if forced to do that. Neighboring countries usually follow similar patterns. For example, oil-exporting countries, high population countries, similar climate regions, etc. are often adjacent to each other, and this issue affects the choice of methods to reduce emissions. Identifying patterns and trends which affect emissions in each region is important from two aspects. First, the factors affecting the emission of greenhouse gases, which can be derived from the climate of the region, the level of welfare or other issues, and the identification of prevailing patterns could be effective in identifying the drivers and factors involved in emissions. The second refers to the determination of emission reduction responsibilities based on the potentials and interests of each region and suggests different paths of reduction based on different regions of the world.
According to extensive researches conducted around the world to predict or identify the drivers of greenhouse gas emissions, population, economic growth, climatic conditions, energy and carbon intensity are among the most important and influential parameters. These factors could increase energy consumption (mainly in the form of fossil fuels) and thus increases greenhouse gas emissions. Assessing emission drivers in a country or region could determine the basic needs and potentials for emission reduction, and this should be taken into account in shaping international reduction instructions and contracts. In the present study, the main factors and drivers of greenhouse gas emissions over four decades and among countries around the world are investigated for the first time and tried to identify patterns and trends in neighboring regions and countries. In this attempt, spatial statistics have been used to determine correlations and distribution patterns.


Methodology
To evaluate and determine the distribution pattern of effective factors and emissions drivers in the first step, it is necessary to collect data related to each one. These data have been collected for all countries of the world (based on the availability of information) over four decades (2012-1971). Studied factors in this study include population, GDPcapita, energy intensity, carbon intensity and carbon dioxide emissions from fossil fuel combustion of each country. Population data are based on the results of the 2015 World Census by the United Nations (UN, 2016). Information on the economic situation of countries, including GDP (US $, 2005), and carbon intensity is extracted from the World Bank Statistics Database (World Bank, 2016). Energy intensity data are calculated based on information provided by the Energy Information Administration of the United States (EIA) (EIA, 2017). To determine the amount of greenhouse gas emissions, fossil fuel consumption data of each country and the emission coefficients for fuel types are used.
In the next step, global warming potentials (GWP) are considered to determine the equivalent carbon dioxide emissions (CO2eq). In the present study, data related to carbon dioxide emissions of fossil fuels were obtained from the Carbon Dioxide Information Analysis Center (CDIAC- 2017). For some areas, based on the country's fuel consumption data and the guidelines proposed by the Intergovernmental Panel on Climate Change (IPCC), emissions have been calculated.
The spatial distribution of greenhouse gas emissions over a long period is evaluated using ArcGIS 10.3, spatial statistics tools. The studied period is classified into four decades (2001-2010, 1991-2000, 1981-1990 and 1971-1980) and the pattern of spatial distribution and clustering maps for the countries of the world were examined. In the first step, the global spatial autocorrelation analysis (global Moran) and then Moran index values were obtained. In the next step, local spatial autocorrelation (local Moran’s I) is applied to investigate the distribution of clusters. Due to a large number of maps, only the maps of the last decade (2001-2012) are shown and other decades are theoretically examined.
Conclusion
In the present study, the most important factors and drivers of greenhouse gas emissions (including population, GDP, energy intensity and carbon intensity) and distribution patterns were analyzed over four decades (1971-2012) and among all the countries. According to the results of Global Moran’s I index and based on the upward trend of autocorrelation, the countries in the case of GHG emissions follow an increasing trend, the relationships are becoming stronger over time and the convergence of countries in the field of emissions is improving. The peak of convergence back to around 1990, when the Intergovernmental Panel on Climate Change (IPCC) was formed by the World Meteorological Organization (WMO) and the United Nations Environment Program (UNEP). The trend of global Moran’s I autocorrelation for other parameters are examined and the results indicated a positive and clustering pattern. Population, GDPcapita and energy intensity in all countries over 41 years have been accompanied by an increasing trend and convergence. The results also show the High-High clusters of spatial autocorrelation for carbon dioxide emissions in studied decades. Population growth as an important driver of climate change often has been studied in developing countries in East Asia and Africa. The role of the population in increasing emissions has been documented and has always been discussed in emission scenarios. The present study showed that the greatest impact of the population could be seen in the Asian region and it is the most important driver of this continent. Climatic conditions and the development pattern of a country play an important role in energy intensity. Considering issues such as energy prices and the concepts such as consumption optimization, the impact of this index can be studied in different parts of the world. Most Asian and a few African countries experience high energy consumption along with low incomes and productivity due to access to oil and gas resources. While, the development pattern in most of these countries is based on industrial development with high consumption of fuel and minerals. However, most of developed countries (mainly in Europe and the United States) are moving towards clean and low carbon services and industries. Also, development and investment in new and renewable energy or clean fuels can lead to the convergence of developed countries.
Various factors influence the emission of greenhouse gases, and this study has evaluated the behavior of different countries in this field. In conclusion, the prominent role of Asian countries in population growth, Western Europe and North America in GDPcapita, high energy intensity in Asian countries and a few African countries, and finally high carbon intensity in Asian countries and the United States, have led to an increase in emissions. The existence of similar activities and needs, the same climatic conditions, and the existence of fossil fuel resources (such as oil and coal) in certain parts of the world have been fueled these convergences. With this approach, identifying effective and local factors could be effective in formulating emission reduction scenarios and responsibilities, and each region of the world will take steps to reduce emissions in accordance with the existing potentials. However, the current treaties classify most countries in terms of developed or developing, and there is no focus on the factors and incentives for countries to be evaluated, and as a result, many countries consider it unfair to participate in these treaties and reduction goals and don’t ratify them.

کلیدواژه‌ها [English]

  • climate change
  • driving forces
  • Kaya
  • Spatial Statistics
  • world