To analyze the effect of an increase in the quantity or quality of public investment on growth, this paper extends the World Bank’s Long-Term Growth Model (LTGM), by separating the total capital stock into public and private portions, with the former adjusted for its quality. The paper presents the LTGM public capital extension and accompanying freely downloadable Excel-based tool. It also constructs a new infrastructure efficiency index, by combining quality indicators for power, roads, and water as a cardinal measure of the quality of public capital in each country. In the model, public investment generates a larger boost to growth if existing stocks of public capital are low, or if public capital is particularly important in the production function. Through the lens of the model and utilizing newly-collated cross-country data, the paper presents three stylized facts and some related policy implications. First, the measured public capital stock is roughly constant as a share of gross domestic product (GDP) across income groups, which implies that the returns to new public investment, and its effect on growth, are roughly constant across development levels. Second, developing countries are relatively short of private capital, which means that private investment provides the largest boost to growth in low-income countries. Third, low-income countries have the lowest quality of public capital and the lowest efficient public capital stock as a share of GDP. Although this does not affect the returns to public investment, it means that improving the efficiency of public investment has a sizable effect on growth in low-income countries. Quantitatively, a permanent 1 ppt GDP increase in public investment boosts growth by around 0.1–0.2 ppts over the following few years (depending on the parameters), with the effect declining over time.

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.

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%.

The agronomic and oenological behavior of the Pinot noir grape variety was studied in relation to different rootstocks on the Agroscope estate in Leytron (VS): 3309 C, 5 BB, Fercal, 41 BMGt, Riparia Gloire, 420 AMGt, 101-14 MGt and 161-49 C. Rootstock primarily influenced vigor, speed of vine establishment, and mineral nutrition of the graft. Riparia Gloire, 41 BMGt, 420 AMGt and 161-49 C rootstocks were less vigorous and, for the last three, induced a lower nitrogen and potassium supply leading to the production of slightly more acidic wines. The less vigorous rootstocks and 101-14 MGt were slightly more sensitive to water stress.

Learning from experience to improve future infrastructure public-private partnerships is a focal issue for policy makers, financiers, implementers, and private sector stakeholders. An extensive body of case studies and “lessons learned” aims to improve the likelihood of success and attempts to avoid future contract failures across sectors and geographies. This paper examines whether countries do, indeed, learn from experience to improve the probability of success of public-private partnerships at the national level. The purview of the paper is not to diagnose learning across all aspects of public-private partnerships globally, but rather to focus on whether experience has an effect on the most extreme cases of public-private partnership contract failure, premature contract cancellation. The analysis utilizes mixed-effects probit regression combined with spline models to test empirically whether general public-private partnership experience has an impact on reducing the chances of contract cancellation for future projects. The results confirm what the market intuitively knows, that is, that public-private partnership experience reduces the likelihood of contract cancellation. But the results also provide a perhaps less intuitive finding: the benefits of learning are typically concentrated in the first few public-private partnership deals. Moreover, the results show that the probability of cancellation varies across sectors and suggests the relative complexity of water public-private partnerships compared with energy and transport projects. An estimated $1.5 billion per year could have been saved with interventions and support to reduce cancellations in less experienced countries (those with fewer than 23 prior public-private partnerships).

The provision of infrastructure and related services in developing Asia via public–private partnership (PPP) increased rapidly during the late 1990s. Theoretical arguments support the potential economic benefits of PPPs, but empirical evidence is thin. This paper develops a framework identifying channels through which economic gains can be derived from PPP arrangement. The framework helps derive an empirically tractable specification that examines how PPPs affect the aggregate economy. Empirical results suggest that increasing the ratio of PPP investment to GDP improves access to and quality of infrastructure services, and economic growth will potentially be higher. But this optimism is conditional, especially on the region’s efforts to further upgrade its technical and institutional capacity to handle complex PPP contracts.