Evaluating sector-based impact of environmental indicators on Iran GHGs emission: a scenario developing approach

The goal of this study was to look at the major impact of agriculture, industry, and households on GHG emissions and pollutants in Iran using different scenarios. The current study used a descriptive-analytical method, and eight sources of GHGs and pollutants were investigated in three sectors: agricultural, household, environmental, commercial, and industrial. These sources included nitrogen oxides (NOx), sulphur dioxide (SO2), carbon dioxide (CO2), sulphur trioxide (SO3), carbon monoxide (CO), methane (CH4), suspended particle matter (SPM), and nitrogen oxide (N2O). Except for nitrous oxide gas, which was studied for nine years (2007–2016), the other seven gases were studied in three sections over a twenty-year period (1996–2016). Books, articles, and other sources were used to gather information in the theoretical basis of research, and the necessary data were obtained from the Iranian Statistical Center. The linear diagram in Excel, the trend line, and the determination coefficient (R2) were used to determine trends in the share of pollutants emitted by agricultural, household, and industrial sectors. In the second part of the analysis for scenario design, the interaction effects questionnaire was designed with the future probable situation of sectors in polluting emissions (increase in distribution, continuation, and reduction of distribution) in mind. The questionnaire was evaluated using a binary comparison. The statistical population consisted of ten experts in environmental issues. After collecting questionnaires that served as input data for the scenario wizard, related scenarios for each section were extracted. The wizard scenario software produced the following results: 5 strong adaptation scenarios, 614 poorly adapted scenarios, and 51 scenarios with maximum incompatibility in agriculture; 5 strong adaptation scenarios, 560 poorly adapted scenarios, and 119 maximum inconsistency scenarios in the home sector; finally, there are 6 strong adaptive scenarios, 1170 poorly adapted scenarios, and 85 scenarios with maximum incompatibility in the industrial sector.