It is inevitable that administrative managements have become disordered and function-positioning improper since the categories of protected areas in China are abundant and with overlapping protected objectives. Therefore,straightening out the protected area category system is significant to coordinate relationships between protection and development. National parks are a branch under the protected area system that undertakes dual tasks in natural ecological resource protection and utilization. Based on a literature review,this article teases out the connotation and functions of national parks worldwide and states that national park under the IUCN protected area category system is a more complete and accurate generalization for global single national parks. The basic criteria of designating any protected area to be national park are concluded according to the definition of national parks under the IUCN protected area category system. The basic criteria should consist of area suitability,nature resource representative,degree of human influence and function comprehensiveness. We then use nature reserves in China as cases to filter candidates for national parks in China. First,a global ‘national park’ should be no less than 1 000hm2. Second,nature reserves at a national level in China have been designated to be Nationally Representative for unique nature resources and ecosystem. The degree of human influence in every national nature reserve is evaluated by virtue of human footprint index dataset which indicates a degree of human activities influence to every of 100hm2 terrestrial area. Therefore,a total of 55 nature reserves in China at national levels with areas not less than 1 000hm2 are finally determined to be candidate national parks. In the end,an approach of five ‘nested’ categories of protected areas are proposed in order to meet the final criteria of functional comprehensiveness.
By building a model that can estimate the potential market of Chinese marine tourism,its market size and spatial distribution can be studied. With the help of online research platforms and Likert comprehensive factor modeling,this study analyzed preference features of five factors：the desire to travel,travel time,residence time,types of the destinations and preferences to tourism projects for potential marine tourism market. The impact of five internal restrictive factors （tourism consumption,leisure time,traffic,spatial distance and relevant groups,on travel capability of marine tourism in genesis perspectives）and seven external restrictive factors （scenic scenery,facilities,culture,prices,service level,the ecological environment and scenic order through comparison of marine tourism destination and international tourism destination）were investigated. The results indicate that the total size of the Chinese potential marine tourist market in 2013 is approximately 663,119,600 people,accounting for 1/5 of the total domestic tourism market. As for the spatial scale,it shows significant double-orientation in economy and distance,which expresses a declining trend from east to west. Our desire for marine tourism is strong,expected travel time concentrates on spring and winter,the length of residence time ranges from four to seven days,the expected type of destinations is sun,sand,sea and landscape predominantly,and the preferred tourist program remains recreation and sightseeing. These findings tell us that our expectation of marine tourism is still at a primary level. The main restrictive factors of marine tourism markets are economy and leisure time,and the restrictive factors of destination are the disorder of scenic spots and insufficiency of service level. In the future,there should be some improvements in promotions and vacation schedule so that more low-income residents and working-class people can experience the charm of marine tourism. Promotion in market regulation,standardization and public services is also necessary. We can strengthen the competitiveness of Chinese marine tourism destinations and reduce the loss of the high-end marine tourist market.
This paper depicts the regional inequality of the domestic tourism economy and the regional inequality of urban-rural income in China from 1999 to 2013 using the coefficient of population-weighted variation. We analyzed the relationship between inequality of domestic tourism economy and regional inequality of urban-rural income using the Engel-Granger Two-step Co-integration Model,Error Correction Model and Granger Causality Model. We found that tourism economic growth and urban-rural income gap is convergent in all regions. Among all regions,the trend of convergence is most significant in the national scope. There is a long-term equilibrium relationship between the inequality of domestic tourism expansion and inequality of urban-rural income gap in China and all other regions. Particularly,the coefficient of population-weighted variation of urban-rural income gap increased 0.631 5% while the degree of variation of tourism economy growth increased 1%. The error correction model showed that for the short-term equilibrium relationship,increasing by 1% the degree of variation tourism economic growth,increases the degree of variation of urban-rural income gap by 0.631 5%. Moreover,the more advanced the regional economy,the stronger the adjustment in long-term equilibrium in the short-term. This is because with a more stable basis and better environment for development,the expansion of tourism in these areas is more likely to have positive effects on reducing urban-rural income gaps. The results of the Granger Causality Model showed that the growth of degree of variation of the tourism economy is the cause of increasing the degree of variation of the urban-rural income gap,but not vice versa. These data show that growth in the tourism economy in China can reduce urban-rural income gaps.
In order to guide the energy consumption behavior of urban residents towards low carbon-orientation and promote the realization of China's energy conservation and emissions reduction targets,a theoretical model of the psychological motivation factors of urban residents was established. It was based on the classical Theory of Planned Behavior and the Theory Value Belief Norm. Urban residents from Xuzhou city,Jiangsu were selected as respondents because of this area’s typical development and geography. 710 valid questionnaires were obtained via random sampling from different urban areas of Xuzhou and analyzed using a structural equation model （SEM）. In the meantime,the theoretical model was empirically tested. We found that a low-carbon behavioral intention is the antecedent variable for energy consumption behavior;the relationship between each dimension of behavior is strong,thus,inspiring urban residents’ intention is the key to implementing low carbon-oriented energy conservation behavior. Subjective norms,perceived behavioral control and altruism values enhance the behavior in a direct and positive way,among them subjective norms are the most useful,environmental values effect is least useful,and biosphere has no influence. Under the positive modulatory role of perceived energy price and an energy efficient product economy,a low carbon behavioral intention transforms more into buying behavior,while the effect on habitual low carbon behavior is relatively weak. Urban residents’ low carbon energy consumption behavior varies with different household income,household size,residence property rights and household structure.
China's rapid economic development has accelerated the energy development process. Given the particularity of their location,demographic composition and other conditions,national minority mountainous areas have not been able to rid the shackles of old systems and modes,and this has led to poverty for the majority of residents. Here,four different regions in the Sichuan Liangshan Yi autonomous prefecture were studied. Information about household energy consumption was obtained by questionnaire. Important factors that affect energy consumption were identified using a Binary Choice Model and Tobit Model. Trends in energy transformation and intrinsic motivation were analyzed. The results showed that models can better identify potential influential factors under the condition of many explanatory variables and limited dependent variables. Family size,education level,income level and national habits had significant impacts on living energy consumption. The higher the income,the more obvious propensity to consume secondary energy. The higher the education level,the more people accept clean energy. The consumption structure reflected the difference between urban and rural resident living levels. Household energy consumption can be affected by internal and external factors,they are traffic inconvenience resulting from topographical features and per capita income and level of education. The changing trend indicated a transformation of consumption behavior from survival consumption to development consumption. Biogas,solar and other clean energy are more and more popular. The energy development experience of Liangshan Yi autonomous prefecture was summarized to guide the development of China's underdeveloped minority areas,promoting the sustainable development of new energy.
China is a big country in coal production and consumption. How to support coal supply is a strategic task for China and this influences transport networks and the import-export trade in coal. Ports ares the most important transit nodes in the transportation of coal and here we studied the development of coal trade by port. We first review the process of coal import and export since the end of the 1970s in China and describe China's transformation from a net exporter of coal to a net importer. The spatial patterns of coal import traffic and export traffic for China`s ports are analyzed,especially spatial features and centralized regions. The results showed that in recent years the spatial pattern of ‘South import and North export' of Chinese port's coal imports and exports transformed rapidly into that of ‘South import,North import and export'. We depict the national network of China`s coal trade and one-way unloading and two-way distribution network. The results indicate that several seaports have become a hub between one-way unloading and two-way distribution. We dicuss the dynamics of rapid evolution of China's coal trade pattern,from several aspects of supply-demand relationship,heavy industries of coastal region,policies related to coal trade and market price,and transport bottlenecks. However,the current explosive growth of imported coal in China is temporal market behavior,the volume of coal will maintain a high-order position but its growth rate will decline in the future. This paper can help readers understand the current characteristics of China's coal imports and exports,and guide the construction of ports and coal transportation planning.
Improving China's carbon productivity is regarded as a significant move to achieve the win-win goal of carbon emissions reduction and economic sustainable growth for China's low-carbon transition. This paper introduces the energy and human capital features （including education and learning by doing）into the C-D production function to obtain the baseline and improved carbon productivity determining equations. We also empirically examined the role played by clean energy use and factor allocation structure on carbon productivity using provincial panel data of Mainland China covering the period from 2003 to 2012. Our empirical results showed that the effect of clean energy use has emerged to improve China's provincial carbon productivity;furthermore,the effect is significantly different in regions of different clean energy development stages. Sub-regional regression results showed the effect of clean energy is smaller in the Eastern region than the Midwest region. Factor allocation structure is an important condition affecting China's provincial carbon productivity. Note that there is a significant positive correlation between capital-energy ratio （K/E）and labor-energy ratio （L/E）and carbon productivity,respectively,but a declining labor-energy ratio has adversely affected China's carbon productivity growth in recent years. Improvement of education human capital contributes to carbon productivity growth,however,the contribution is extremely weak. “Learning by doing （LBD）” did not support the expected positive impact on China's carbon productivity as the carbon productivity determining equation previously showed. In addition,we discussed the problem of endogeneity in our models. To ensure China's carbon productivity growth,we should expand the supply scale of clean energy （i.e.,nuclear,wind and solar）continuously and adjust the factor allocation structure prudentially.
In 2014,China's state council issued that China's total energy consumption control objectives in 2020 would be about 4.8 billion tons of standard coal. The target whether can be realized or not is the focus of this paper. From 2011,China's economy has entered the new normal status：its growth rate has slowed to less than 10% with economic structural adjustment and growth pattern transformation. At the same time,China's energy consumption also has been changing：its growth rate has fallen to 2.2%,and coal consumption proportion dropped year by year. Under the decreasing of both economy growth and energy consumption,this paper recalculates energy elasticity coefficient and renames it the fixed base energy consumption elasticity coefficient,which solves the problem of energy elasticity coefficient data is volatile,irregular and not for quantitative analysis. Then,with using the latest economy and energy data revised by national statistical department,the paper studies its main factors by the methods of co-integration analysis and predicts China's future energy demand. Results show that three variables,which are the fixed base energy consumption elasticity coefficient,industrial structure and technical progress,have long-term equilibrium relationship. What's more,industrial structure has a more negative effect than technical progress on elastic coefficient. Based on all above research ,we get the following conclusions that under low,medium and high scenarios,China's total primary energy consumption will separately reach 4.71,4.82 or 4.92 billion tons of standard coal in 2020,average annual growth rate of 1.7%~2.4%;That is,China's total energy control objectives can be basically fulfilled.
Under the background of global warming,increasing food productivity to ensure food security in China has very important practical significance. Chinese provincial panel data from 1978 to 2012 was used in this study and the climatic yield of grain production was separated using the HP filter method. Then with the assumption of ‘technology will not be forgotten',sequential DEA was employed to measure technical efficiency,technical progress and the Malmquist productivity index of China's grain production in two cases,considering climate factors or not. We found that climatic yield behaves as an inverted ‘U' type fluctuating around the 0 level,and climate conditions have both positive and negative impacts on grain production. Sequential DEA analysis shows that decision to consider climate factors has a significant impact on measurement results. When climate factors are considered,grain total factor productivity becomes lower,with worse technical efficiency but promoted technological progress. Last but not the least,regional difference in China's grain total factor productivity growth are apparent. Compared with central and western regions,eastern China is better. Therefore,we have every reason to believe that the eastern region basically dominates growth of the grain economy total factor productivity. At the same time,eastern China leads technical innovation in grain production.
Poverty reduction in special areas has been recognized as an important issue in the national precise poverty alleviation strategy in China. Based on data from 46,704 rural households collected by the National Bureau of Statistics in special areas,we analyzed the influences that meteorological disasters have on rural poverty in special areas in China from the angle of sustainable livelihood capital. The results indicate that meteorological disasters significantly decrease the income of farmers in special regions and that meteorological disasters have negative effects on agrarian and non-agrarian income for farmers. The impacts of meteorological disasters on poor households are relatively greater than those of non-poor households. The livelihood capital,especially human capital that farmers possess obviously has positive impacts on fighting with meteorological disasters. From a policy perspective,it means that enhancing poverty reduction steadily should be achieved by means of carrying out free training for farmers,improving rural education levels to reinforce the rural human capital of poor households,constructing regional disaster-defense systems,offering agricultural insurance or family property insurance,and providing meteorological information support for migrant workers. There is a also a great need to adjusting the targeting system of poverty alleviation which plays a vital role in pushing forward poverty alleviation policies. Regulating the policy of precise poor helping mechanism is of great significance and bettering ecological compensation policy will be a terrific helper in alleviating poverty. Governments should strengthen poverty reduction strategies steadily by adopting the measures suggested here.
Based on temperature data from 1951 to 2012 we analyzed abrupt change features of temperature and temporal and spatial variation before and after abrupt temperature change. We found that year （season）minimum temperature changed （1981-1987）,followed by year （season）mean temperature （1981-1994）,and then year （season）maximum temperature （1985-1999）. Winter temperatures have changed more abruptly than summer temperatures;minimum temperature was more abrupt than mean temperature,mean temperature was more abrupt than maximum temperature in spring （autumn and annual）;winter shows the opposite pattern. Before the abrupt change in minimum temperature （0.50℃/10a）in spring,after the abrupt change of the maximum temperature in autumn （0.75℃/10a）,are the largest contributor to temperature rise. Across all of the elements of temperature before and after abrupt change,the largest incremental mean value for many years is the winter minimum temperature （1.86℃）and the smallest is annual maximum temperature （0.83℃）. The largest incremental climate tendency rate is the autumn maximum temperature （0.72℃/10a）and the smallest is the summer maximum temperature （-0.02℃/10a）. Seasonal order of incremental mean temperature value for many years before and after abrupt change is opposite to their incremental climate tendency rate. The value range of all annual temperature elements' climate tendency rates after abrupt change are wider than before abrupt change;the area of annual minimum temperature increasing significant abrupt change increased by 6.79% compared with their before abrupt change. For the Xinkai and Xiliao Rivers the warming trend is higher than for other regions after abrupt change,except for the mean temperature for the Horqin Left Wing Middle Banner region. This study not only has a practical guide on ecological environment, farming and stockraising development, water resources development and utilization and and so on production and fliving but also has a certain scientific reference on global climatic change law.
The content and spatial distribution of soil salinity is closely related to agriculture development and land productivity at a regional scale. It is essential to determine the content and spatial distribution of soil salinity in a timely manner as soil salinization could cause land degradation and influence human lives. Geographically weighted regression （GWR）is a local regression interpolation method that can achieve spatial extension of the dependent variable based on the relationships between the dependent variable and environmental variables and the spatial distances between sample points and predicted locations. GWR has been successfully applied to studies on some soil properties,such as soil organic matter. This study aimed to explore the feasibility of GWR in predicting soil salinity through comparisons with multiple linear regression （MLR）and Cokriging. Environmental factors,including the normalized difference vegetation index （NDVI）,elevation and the distances of sample points from the rivers,were selected as auxiliary variables for GWR. The result generated by GWR showed a strong regularity in the spatial distribution of soil salinity,which has an increasing trend from coastal to inland areas,and the values of soil salinity near rivers were smaller than other regions. When compared to the map produced by Cokriging,GWR weakened the smoothing effect and many details became apparent. These findings indicate that GWR is applicable to predicting soil salinity. The prediction accuracy was higher than those of MLR and Cokriging. The RMSE,correlation coefficient,regression coefficient and adjust coefficient were 0.305,0.649,0.572 and 0.421,respectively. In addition,the prediction map generated by GWR reduced the smoothing effect compared to that of Cokriging and showed more spatial details than that of MLR.
Land use/cover change（LUCC）is at the core of global change and agricultural land change is important in the study of LUCC. With socio-economic development,pressure on agri-cultural land protection has been increasing in China. To develop sustainable land use policy, we need to understand regional agricultural land changes and driving forces. Using land use monitoring data,basic geographic spatial data and statistical yearbooks,we analyzed characteristic of agricultural land change in the Pearl River Delta Area,China. Traditional Logistic modeling and AutoLogistic modeling which bring in spatial autocorrelation were compared to investigate the drivers of agricultural land change at regional scale from the perspectives of natural,socio-economic,spatial distance and spatial autocorrelation of land use characteristics. We found that cultivated land and forest in this region decreased from 2000 to 2010,leading to further fragmentation. Cultivated land has become a main source of expansion of construction land. And,both spatial autocorrelation of agricultural land property and land development intensity are important driving forces of agricultural land change. As for other driving forces,cultivated land change was mainly affected by factors such as ‘change of per kilometer GDP',‘distance to nearest railway',‘change of total population density',‘tendency rate of annual sunshine hours'. Forest changes were mainly affected by ‘change of per kilometer GDP',‘change of total population density',‘slope' and ‘distance to nearest road'. Compared with traditional Logistic modeling,the AutoLogistic model is more suited to study driving forces of regional agricultural land change.
The intention and affecting factors of rural residential land quittance in different regions were analyzed using a Logit model based on survey data in 125 villages,seven cities in Jiangsu province. We found that age,whether to have property income,whether to have an urban residence,the condition of rural residential land,replacement cost of rural residential,agricultural subsidy,new rural endowment insurance amount and regional traffic environment have negative effects on quitting residential land. Education level,occupation,rural residential land area and residence time of residential land have positive effects on quitting residential land. In accordance with impact strength,the sort is:the use condition of rural residential land > agricultural subsidy > whether to have property income > occupation > new rural endowment insurance amount > education level > age > whether to have an urban residence > regional traffic environment > the residence time of residential land > replacement cost of rural residential > rural residential land area. In developed areas,education level,family members,residence time of residential land and certificate of title have positive significant effects on quitting the residential land. Age,whether to have property income,the condition of rural residential land,replacement cost of rural residential and regional ecological environment have negative effects on quitting residential land;in economically underdeveloped areas,occupation,rural residential land area,residence time of residential land and farmland occupation have positive significant effects on quitting residential land. Agricultural subsidies have a negative effect on quitting residential land. This conclusion provides reference for the government to formulate and perfect rural residential land quittance policy and improve the utilization efficiency of rural residential land.
Revealing the influence of micro factors on urban land price and housing price is essential to improve the predicting precision of urban land price change and house price change and exercise effective macro-control over land market and real estate market. The aim of this study was to use the Geographically Weighted Regression model to quantitatively measure the impacts of local special natural features （lakes,rivers and mountains）on urban residential land and housing prices and their spatial differences,and to explain the cause from the aspect of impact factors. We found that the spatial distributions of Moran's I,P value,R2,adjusted R2 and regression parameters of the testing models can characterize the spatially non-stationary relationship between natural features and residential land and housing prices accurately. When it comes to effect strengths,the average marginal residential land and housing price affected by lakes is 0.11 CNY/m2 and 0.52 CNY/m2,respectively,while that affected by lakes is 0.15 CNY/m2 on both residential land and housing prices. Lakes and rivers have more distinguished impacts on residential land and housing in areas with high prices according to the dynamic trends of their effects. Meanwhile,affected by the existence of surrounding commercial districts,anisotropic relationships between large lakes like East Lake and housing prices exist. Those mountains with a scattered distribution and low gradient,limited estate developments by the protected specific artificial amenities alike Yellow Crane Tower,show an insignificant influence. Rather than fitting with the spatial change trends of housing price,the spatial dynamics of residential land and housing prices are similar with that of regional trends and the change range is positively correlated with price. Rivers generate analogous impacts on both residential land and housing prices with similar marginal effects,but in two different spatial trends of gradients and spheres.
Soil and water loss in the Daqinghe rocky mountainous area in the Haihe Basin,China is serious. Here,we discuss the runoff-sediment relationship at five spatial scales:micro runoff plot,normal runoff plot,micro watershed,watershed,medium and large basin. The results show that there are strong positive linear correlations between sediment yield modulus (Ms) and surface runoff depth (Rs) at these spatial scales,and the proportional function can be used to fit their linear relationship. There is a good linear correlation between Ms and rainfall,but there are no obvious correlations between Ms and the average rainfall intensity. In the several land-use patterns (i.e. Platycladus orientalis,pine,shrubbery,uncultivated land and bare land),the Ms and runoff coefficient of bare land are significantly higher than those of others. The Ms and runoff coefficient of the arborvitae plot are smaller than those of the pine plot, the reason is the canopy interception of arborvitae is slightly larger than the pine. In addition, forest litter cover can greatly reduce water loss and soil erosion of slopes in this region. At the plot scale, with the deciduous vegetation or litter cover there are similar runoff-sediment relationships. In our opinion, vegetation cover can reduce sediment due to a reduction in runoff for slopes in the Daqinghe rocky Mountainous area. For the lack of grassland and farmland runoff plot data in this study,the results primarily analyzed the relationship between runoff and sediment. This study can help lay a theoretical foundation for predicting soil and water loss in the comprehensive management of the Haihe River basin.
Based on daily mean temperature measurements at 44 meteorological stations throughout the Hebei Shanxi mountainous region and Loess Plateau region,Northern China from 1961 to 2013,trends in the tree growing season were analyzed in two regions （humid and subhumid）. The growing season was defined as the last day of the first 5-day period with a daily mean temperature greater than 5℃ to the last day of the first 5-day period with a mean temperature of less than 5℃. We found that over the last 50 years,the commencement of the tree growing season has increased at a rate of -1.7d/10a,-2.1d/10 and -1.9d/10a,throughout the Hebei Shanxi mountainous region,Loess Plateau region and the whole study area,respectively. The end of the tree growing season has decreased at a rate of 0.9d/10a,1.1d/10a and 1.0d/10a in the three areas,respectively. The length of the tree growing season in the study area increased from 1961 to 2013 at a rate of 2.6d/10a （13.3 days）,3.1d/10a （16.4 days）and 2.9d/10a （14.8 days）in the three study areas respectively. Elevation had a strong impact on the tree growing season indices （growing season start,end and length）,except for a little effect on commencement of the tree growing season in the Hebei Shanxi mountainous region. Tree growing season indices （start,end and length of the growing season）were associated with spring and autumn temperatures. The spatial distribution of the trend in the commencement of the tree growing season showed a decreasing trend. The end point and length of the tree growing season has increased.
Vegetation dynamics across the Loess Plateau,China have been widely observed. The evaluation of vegetation cover change and driving factors at the county level and seasonal scale is useful to evaluate the specific effects of vegetation restoration. Based on MODIS-NDVI data from 2000 to 2012,supplemented by Sen+Mann-Kendall modeling,this study analyzed trends in vegetation cover change in Changwu County,and determined climatic driving factors. We found that the interannual average value of NDVI of the study area was increased with a linear tendency of 0.064/10a;the ecological restoration project achieved remarkable results in Changwu County from 2000 to 2012. Vegetation cover showed an increasing trend in most areas of Changwu County. The number of pixels decreasing was small and mainly distributed in Dizhang town and Zhaoren town resulting from urbanization and industrialization. Trends in vegetation cover changes were different between seasons over the 13 years of data and vegetation improvement was most significant in summer. Although vegetation improvement was apparent in autumn,the area was smaller than in summer. Vegetation improvement in spring was weaker than in summer and autumn. The number of decreasing pixels was large in this season,and mainly distributed in the north of the study area. There was a correlation between vegetation growth and temperature/precipitation. Precipitation was the dominant natural factor driving annual vegetation improvement in Changwu County. Spring rain had a significant influence on vegetation growth. The response of vegetation growth to temperature had a time lag of one month. The effect of precipitation on vegetation growth was relatively small at the month scale,and there was no time lag effect because of irrigation as a human adaption can alleviate the threat of low precipitation. Besides land exploitation which had a negative effect on vegetation growth,planting and mitigation can benefit vegetation improvement. The Grain for Green project should have a positive effect on vegetation improvement in Changwu County.
The four key elements of public opinion,science,government and law were used to construct an empirical model built upon Game Theory. This theoretical framework,in which the wetland management policies of different countries can be compared,demonstrates the mechanism by which the four key elements interact to develop environmental policy and protect wetlands. Based on this empirical model,the history of wetland policies in the USA including key stakeholders,policy implementation approaches,policy instruments and management challenges were discussed. Evolution of the institution that protects American wetlands is broken down into four stages:Westward Movement and Reclamation （pre-1900）,Waterfowl Protection and Public Participation （1900-1970）,Core Laws and Scientific Research （1971-1980）and Improved Institutions and New Challenges （1980-present）. Wetland policies originate with public opinions and beliefs,scientific knowledge is a prerequisite for policy implementation and,lastly,effective governmental jurisdiction and institutionalization of wetland protection regulation and policy are critical. Although public education,scientific definition and governmental jurisdiction are challenging,Chinese wetland managers and policy makers can learn from the implementation of USA wetland policies. The analysis outlined in this paper is a template that other natural resource studies can follow when developing natural resource strategies.
China is the largest fertilizer producer and consumer and its fertilizer consumption has exceeded 59.96 million tons,almost one third of the world's total volume. A critical task is to improve agricultural sustainable development while ensuring food security and reducing unreasonable fertilizer usage. Soil Testing and Fertilization Recommendation（STFR）is a scientific method that ensures food security and ecological security. Based on national STFR data for grain crops as a reasonable volume of fertilizer input,we systematically calculated the potential reduction of fertilizer input on three main grain crops,wheat,maize and rice. The results indicated that the total reduction amount of fertilizer input for wheat,maize and rice can be 8.141 million tons,accounting for 27.6% of the amount of total fertilizer use in three main crop production areas while grain productivity can meet current levels. The percentage of fertilizer reduction for wheat,rice and maize is 36.9%,28.1% and 16.7% respectively. Synthetic fertilizers are highly energy- and carbon-intensive products. The volume of carbon emissions was also assessed using China greenhouse gases emission factors. The result showed that 10.459million tons of carbon emissions could be reduced due to fertilizer input decreases using STFR. Huge potential in carbon emission reductions are concentrated mainly in major provinces of grain production located on the North China plain and Northeast plain. These results imply that policy-makers and government should widely spread STFR,establish a service center for STFR produce,increase the subsidy for using STFR,create new promotion methods,and establish a national STFR big-data platform.
