Local community participation in forest management is pivotal since they are familiar with the forest environment.In the successful management of community forestry(CF),both males and females along with the representa...Local community participation in forest management is pivotal since they are familiar with the forest environment.In the successful management of community forestry(CF),both males and females along with the representation of poor and disadvantaged groups are of vital importance.This research compares the users’perception in community forest management(CFM)activities,and socio-economic variables influencing participation in studied community forestry user groups(CFUGs).Primary data were collected through reconnaissance surveys,interviewing key informants,focus group discussions,and household surveys.Secondary data were collected from the division forest office,CFUGs’operational plan(OP)and Constitution,internet,and authenticated websites.The chi-square(χ^(2))test was applied to test separately association variables like gender,caste,age class,education level,and wealth ranking with participation.Using ordered logit regression,the variables affecting participation in OP and constitution-making,Silvicultural activities,Forest products collection,and CF fund mobilization were quantified.Gender and Education were found to be the most promising factor influencing participation in Jagriti CFUG and Jhankrikhola CFUG respectively.In general,higher caste,older age,and rich people dominate the major decision-making activities.However,lower caste and poor people have been involved comparatively more in Forest product collection.展开更多
After more than 30 years of rapid urbanization, the overall urbanization rate of China reached 56.1% in 2015.However, despite China's rapid increase in its overall rate of urbanization, clear regional differences ...After more than 30 years of rapid urbanization, the overall urbanization rate of China reached 56.1% in 2015.However, despite China's rapid increase in its overall rate of urbanization, clear regional differences can be observed. Furthermore, inadequate research has been devoted to in-depth exploration of the regional differences in China's urbanization from a national perspective, as well as the internal factors that drive these differences. Using prefecture-level administrative units in China as the main research subject, this study illustrates the regional differences in urbanization by categorizing the divisions into four types based on their urbanization ratio and speed(high level: low speed; high level: high speed; low level: high speed; and low level: low speed). Next, we selected seven economic and geographic indicators and applied an ordered logit model to explore the driving factors of the regional differences in urbanization. A multiple linear regression model was then adopted to analyze the different impacts of these driving factors on regions with different urbanization types. The results showed that the regional differences in urbanization were significantly correlated to per capita GDP, industry location quotients, urban-rural income ratio,and time distance to major centers. In addition, with each type of urbanization, these factors were found to have a different driving effect. Specifically, the driving effect of per capita GDP and industry location quotients presented a marginally decreasing trend, while main road density appeared to have a more significant impact on cities with lower urbanization rates.展开更多
文摘Local community participation in forest management is pivotal since they are familiar with the forest environment.In the successful management of community forestry(CF),both males and females along with the representation of poor and disadvantaged groups are of vital importance.This research compares the users’perception in community forest management(CFM)activities,and socio-economic variables influencing participation in studied community forestry user groups(CFUGs).Primary data were collected through reconnaissance surveys,interviewing key informants,focus group discussions,and household surveys.Secondary data were collected from the division forest office,CFUGs’operational plan(OP)and Constitution,internet,and authenticated websites.The chi-square(χ^(2))test was applied to test separately association variables like gender,caste,age class,education level,and wealth ranking with participation.Using ordered logit regression,the variables affecting participation in OP and constitution-making,Silvicultural activities,Forest products collection,and CF fund mobilization were quantified.Gender and Education were found to be the most promising factor influencing participation in Jagriti CFUG and Jhankrikhola CFUG respectively.In general,higher caste,older age,and rich people dominate the major decision-making activities.However,lower caste and poor people have been involved comparatively more in Forest product collection.
基金supported by the National Science and Technology Support Program(Grant No.2014BAL04B01)the National Natural Science Foundation of China(Grant No.4159084)the National Social Science Fund of China(Grant No.14BGL149)
文摘After more than 30 years of rapid urbanization, the overall urbanization rate of China reached 56.1% in 2015.However, despite China's rapid increase in its overall rate of urbanization, clear regional differences can be observed. Furthermore, inadequate research has been devoted to in-depth exploration of the regional differences in China's urbanization from a national perspective, as well as the internal factors that drive these differences. Using prefecture-level administrative units in China as the main research subject, this study illustrates the regional differences in urbanization by categorizing the divisions into four types based on their urbanization ratio and speed(high level: low speed; high level: high speed; low level: high speed; and low level: low speed). Next, we selected seven economic and geographic indicators and applied an ordered logit model to explore the driving factors of the regional differences in urbanization. A multiple linear regression model was then adopted to analyze the different impacts of these driving factors on regions with different urbanization types. The results showed that the regional differences in urbanization were significantly correlated to per capita GDP, industry location quotients, urban-rural income ratio,and time distance to major centers. In addition, with each type of urbanization, these factors were found to have a different driving effect. Specifically, the driving effect of per capita GDP and industry location quotients presented a marginally decreasing trend, while main road density appeared to have a more significant impact on cities with lower urbanization rates.
