In recent years, researchers have recognized the complexity of the interactions between the ecological system and the economic development of human society. However, the complicated relationships overwhelm traditional statistical procedures and require an innovative approach to investigate their dynamics. We proposed this study to provide a unique perspective in analyzing the long-term causal relationships between the grassland productivity, climate change, and socioeconomic development of Inner Mongolia Autonomous Region (IMAR) of China. Our attempt began with acquiring remotely sensed satellite imagery, climatic variations, and aggregated annual reports of the socio-economy of the IMAR in vegetation growing seasons for 15 years. The spatial and temporal dissimilarities of the raw observations prevented us from exploiting the potential of this valuable dataset; thus, we interpolated and extrapolated the data to generate a panel dataset with consistent spatial and temporal resolutions. Then, we took another step to preprocess the panel data by applying a signal filter to isolate the long-term trend of change from the inter- and intra-annual cyclic patterns and used the trends as the input for a panel data model. The results from our statistical analysis indicated that the independent variables explained the variations in the dependent variable extremely well, while the polynomial terms of climatic variables were significant with limited marginal effect and most of the climatic variables showed negative linear impact on the grassland productivity. In the meantime, we found not all socioeconomic variables we attempted to include into the model significantly affected grassland productivity, especially the variables describing the financial status of the IMAR residents.
","abstract_html":"<p>In recent years, researchers have recognized the complexity of the interactions between the ecological system and the economic development of human society. However, the complicated relationships overwhelm traditional statistical procedures and require an innovative approach to investigate their dynamics. We proposed this study to provide a unique perspective in analyzing the long-term causal relationships between the grassland productivity, climate change, and socioeconomic development of Inner Mongolia Autonomous Region (IMAR) of China. Our attempt began with acquiring remotely sensed satellite imagery, climatic variations, and aggregated annual reports of the socio-economy of the IMAR in vegetation growing seasons for 15 years. The spatial and temporal dissimilarities of the raw observations prevented us from exploiting the potential of this valuable dataset; thus, we interpolated and extrapolated the data to generate a panel dataset with consistent spatial and temporal resolutions. Then, we took another step to preprocess the panel data by applying a signal filter to isolate the long-term trend of change from the inter- and intra-annual cyclic patterns and used the trends as the input for a panel data model. The results from our statistical analysis indicated that the independent variables explained the variations in the dependent variable extremely well, while the polynomial terms of climatic variables were significant with limited marginal effect and most of the climatic variables showed negative linear impact on the grassland productivity. In the meantime, we found not all socioeconomic variables we attempted to include into the model significantly affected grassland productivity, especially the variables describing the financial status of the IMAR residents.</p>","abstract_has_math":false,"creators":["Fan, Siyu"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Open Access Dissertation","degree_discipline":"College of Technology","degree_department":null,"school":null,"contributors":["Yichun Xie","Benedict Ilozor","Xiangdong Che"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-01-01T08:00:00Z","date_published":"2017-01-01T08:00:00Z","updated_at":"2026-07-24T02:17:13Z","subjects":["Climate change","Empirical mode decomposition","Inner Mongolia","Panel data analysis","Socio-economy","Technology and Innovation"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.emich.edu/theses/751","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yichun Xie","Benedict Ilozor","Xiangdong Che"]},{"key":"dc:creator","label":"Author","values":["Fan, Siyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-02-28T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["College of Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Climate change","Empirical mode decomposition","Inner Mongolia","Panel data analysis","Socio-economy","Technology and Innovation"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.emich.edu/theses/751"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In recent years, researchers have recognized the complexity of the interactions between the ecological system and the economic development of human society. However, the complicated relationships overwhelm traditional statistical procedures and require an innovative approach to investigate their dynamics. We proposed this study to provide a unique perspective in analyzing the long-term causal relationships between the grassland productivity, climate change, and socioeconomic development of Inner Mongolia Autonomous Region (IMAR) of China. Our attempt began with acquiring remotely sensed satellite imagery, climatic variations, and aggregated annual reports of the socio-economy of the IMAR in vegetation growing seasons for 15 years. The spatial and temporal dissimilarities of the raw observations prevented us from exploiting the potential of this valuable dataset; thus, we interpolated and extrapolated the data to generate a panel dataset with consistent spatial and temporal resolutions. Then, we took another step to preprocess the panel data by applying a signal filter to isolate the long-term trend of change from the inter- and intra-annual cyclic patterns and used the trends as the input for a panel data model. The results from our statistical analysis indicated that the independent variables explained the variations in the dependent variable extremely well, while the polynomial terms of climatic variables were significant with limited marginal effect and most of the climatic variables showed negative linear impact on the grassland productivity. In the meantime, we found not all socioeconomic variables we attempted to include into the model significantly affected grassland productivity, especially the variables describing the financial status of the IMAR residents.
"]},{"key":"dc:title","label":"Title","values":["An analysis of long-term effects of climate change and socioeconomic activities on grassland productivity of inner Mongolia"]}]}],"canonical_facts":{"dc:contributor":["Yichun Xie","Benedict Ilozor","Xiangdong Che"],"dc:creator":["Fan, Siyu"],"dc:date.available":["2018-02-28T08:00:00Z"],"dc:description.abstract":["In recent years, researchers have recognized the complexity of the interactions between the ecological system and the economic development of human society. However, the complicated relationships overwhelm traditional statistical procedures and require an innovative approach to investigate their dynamics. We proposed this study to provide a unique perspective in analyzing the long-term causal relationships between the grassland productivity, climate change, and socioeconomic development of Inner Mongolia Autonomous Region (IMAR) of China. Our attempt began with acquiring remotely sensed satellite imagery, climatic variations, and aggregated annual reports of the socio-economy of the IMAR in vegetation growing seasons for 15 years. The spatial and temporal dissimilarities of the raw observations prevented us from exploiting the potential of this valuable dataset; thus, we interpolated and extrapolated the data to generate a panel dataset with consistent spatial and temporal resolutions. Then, we took another step to preprocess the panel data by applying a signal filter to isolate the long-term trend of change from the inter- and intra-annual cyclic patterns and used the trends as the input for a panel data model. The results from our statistical analysis indicated that the independent variables explained the variations in the dependent variable extremely well, while the polynomial terms of climatic variables were significant with limited marginal effect and most of the climatic variables showed negative linear impact on the grassland productivity. In the meantime, we found not all socioeconomic variables we attempted to include into the model significantly affected grassland productivity, especially the variables describing the financial status of the IMAR residents.
"],"dc:identifier":["https://commons.emich.edu/theses/751"],"dc:subject":["Climate change","Empirical mode decomposition","Inner Mongolia","Panel data analysis","Socio-economy","Technology and Innovation"],"dc:title":["An analysis of long-term effects of climate change and socioeconomic activities on grassland productivity of inner Mongolia"],"thesis:degree_discipline":["College of Technology"],"thesis:degree_level":["Open Access Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T02:17:13Z"}