This study analyzes the impact of a high-speed rail line on tax revenues and on the economy of affected regions within the country. The economic impact of infrastructure investment can be induced by changes in tax revenues when the infrastructure is in operation. Accurate regional GDP data are not necessarily available in many Asian countries. However, tax data can be collected. Therefore, this study uses tax revenue dates in order to estimate spillover effects of infrastructure investment. The Kyushu high-speed rail line was constructed in 1991 and was completed in 2003. In 2004, the rail line started operating from Kagoshima to Kumamoto. The entire line was opened in 2011. We estimated its impact in the Kyushu region of Japan by using the differencein- difference method, and compared the tax revenues of regions along the high-speed railway line with other regions that were not affected by the railway line. Our findings show a positive impact on the region’s tax revenue following the connection of the Kyushu rapid train with large cities, such as Osaka and Tokyo. Tax revenue in the region significantly increased during construction in 1991–2003, and dropped after the start of operations in 2004–2010. The rapid train’s impact on the neighboring prefectures of Kyushu is positive. However, in 2004–2013, its impact on tax revenue in places farther from the rapid train was observed to be lower. When the Kyushu railway line was connected to the existing high-speed railway line of Sanyo, the situation changed. The study finds statistically significant and economically growing impact on tax revenue after it was completed and connected to other large cities, such as Osaka and Tokyo. Tax revenues in the regions close to the high-speed train is higher than in adjacent regions. The difference-in-difference coefficient methods reveal that corporate tax revenue was lower than personal income tax revenue during construction. However, the difference in corporate tax revenues rose after connectivity with large cities was completed. Public–private partnership (PPP) has been promoted in many Asian countries. However, PPP-infrastructure in India failed in many cases due to the low rate of return from infrastructure investment. This study shows that an increase of tax revenues is significant in the case of the Kyushu rapid train in Japan. If half of the incremental tax revenues were returned to private investors in infrastructure, the rate of return from infrastructure investment would significantly rise for long period of time. It would attract stable and long-term private investors, such as pension funds and insurance funds into infrastructure investment. The last section of the paper will address how incremental tax revenues created by the spillover effects of infrastructure will improve the performance of private investors in infrastructure investment.

In this paper, all the forests, woodlands and trees in the administrative area of Zhaoling Township in Chuzhou City of Huai'an City were collected and analyzed. The total area of the administrative area is 4852 hectares, the forest coverage rate is 22.07%, and the forest greening rate is 26.13%. This index has exceeded 20% of the forest coverage rate of the well - off society. Tree species is particularly serious. In the forest system (pure forest), the area of pure forest of poplar is accounted for 99.9% of the whole forest area. In the four tree systems, the number of poplar trees accounted for 80% of the total number of trees in the whole tree, and the total amount of poplar trees accounted for 98%. The poplar pure forest age group structure disorders, the unit area is low. The ratio of total area of poplar pure forest in Zhongling and young forests was 92.9%, and the ratio of total area of poplar pure forest and mature forest was 7.1%. The ratio of mature forest and the ratio of mature forest was 0.7%, and the proportion of each group was obviously abnormal.

To increase inter-region connectivity, the Indonesian government initiated infrastructure projects such as toll roads, airport, highways, as well as agriculture ones throughout the countries. One of the big projects in road infrastructure was the Cikampek–Palimanan (Cipali) toll road in West Java with a budget of more than USD1 billion which started to operate in July 2015. This paper is aimed to evaluate the impact of the toll road on accessibilities, trades, and investments in the region it traverses. To carry out the analysis, we used qualitative approach, difference-in-difference approach, and ANOVA, utilizing three kinds of data. The first data is collected from a survey of 331 small-medium enterprises (SMEs) in the logistics and the hotel and restaurant industries. The second one is bank loan data sourced from Bank Indonesia, while the third one is investment data from Investment Coordinating Board of Indonesia (BKPM).
After two years of its operation, Cipali toll road has increased accessibility, mobility, trade, and investment in the region it traverses. The travel time was reduced by 39%, while the cargo volume of the local businesses increased by 30% to 40%. These led to an improvement of wholesale trade volume in almost all regencies. However, SMEs in the hotel and restaurant industry along the traditional northern coastal highway in Subang, Indramayu, and Brebes experienced a decline due to the traffic shifting. Meanwhile, investments from national companies especially those of labor-intensive manufacturing industries flowed significantly especially to Subang and Majalengka, which reflected a “sorting effect”. However, investments from local and foreign businesses did not increase significantly yet after 2.5 years of toll operation.
To reap the benefit from the presence of Cipali toll road, the local governments should improve the ease of doing business to attract investments that boost employment in return. In addition, given a better accessibility from Greater Jakarta and a large number of potential visitors passing through the toll road, local businesses in the trade sector would benefit if they could promote the local attractions such as in tourism activities supported by the local government. The latter strategy should also be implemented by the local governments and local businesses in the northern coastal traditional route to minimize the negative impact of the toll road due to the traffic shifting. This strategy should be strengthened through increasing connectivity from the toll exits to local business areas and through increasing the ease of doing business.

Fire is one of the most serious hazards, which causes many economic, social, ecological, and human damages every year in the world. Fire in forests and natural ecosystems destroys wood, regeneration, forest vegetation, as well as soil erosion and forest regeneration problems (due to the dryness of the weather and the weakness of the soil). Awareness of the extent of the zones that have been fired is important for forest management. On the other hand, the difficulty of fieldwork due to the high cost and inaccessible roads, etc. reveals the need for using remote sensing science to solve this problem. In this research, MODIS satellite images were used to detect and determine the fire extent of Golestan province forests in northern Iran. MID13q1 and MOD13q1 images were used to detect the normal conditions of the environment. The 15-year time series data were provided for the NDVI and NDMI indicators in 2000-2015. Then, the behavior of indicators in the fire zone was studied on the day after the fire. The burned zones by the fire were specified by determining the appropriate threshold and then, they were compared to long-term normals. In the NDMI and NDVI indicators, the mean of the numeric value threshold limit for determining the burnt pixels was respectively 1.865 and 0.743 of the reduction in their normal long-term period, which are selected as fire pixels. The results showed that the NDMI index could determine the extent of the burned zone with the accuracy of 95.15%.

Identify and diagnosis of homogenous units and separating them and eventually planning separately for each unit are considered the most principled way to manage units of forests and creating these trustable maps of forest’s types, plays important role in making optimum decisions for managing forest ecosystems in wide areas. Field method of circulation forest and Parcel explore to determine type of forest require to spend cost and much time. In recent years, providing these maps by using digital classification of remote sensing’s data has been noticed. The important tip to create these units is scale of map. To manage more accurate, it needs larger scale and more accurate maps. Purpose of this research is comparing observed classification of methods to recognize and determine type of forest by using data of Land Cover of Modis satellite with 1 kilometer resolution and on images of OLI sensor of LANDSAT satellite with 30 kilometers resolution by using vegetation indicators and also timely PCA and to create larger scale, better and more accurate resolution maps of homogenous units of forest. Eventually by using of verification, the best method was obtained to classify forest in Golestan province’s forest located on north-east of country.

Every year, hundreds of fires occur in the forests and rangelands across the world and damage thousands hectare of trees, shrubs, and plants which cause environmental and economic damages. This study aims to establish a real time forest fire alert system for better forest management and monitoring in Golestan Province. In this study, in order to prepare fire hazard maps, the required layers were produced based on fire data in Golestan forests and MODIS sensor data. At first, the natural fire data was divided into two categories of training and test samples randomly. Then, the vegetation moisture stresses and greenness were considered using six indexes of NDVI, MSI, WDVI, OSAVI, GVMI and NDWI in natural fire area of training category on the day before fire occurrence and a long period of 15 years, and the risk threshold of the parameters was considered in addition to selecting the best spectral index of vegetation. Finally, the model output was validated for fire occurrences of the test category. The results showed the possibility of prediction of fire site before occurrence of fire with more than 80 percent accuracy.