Financing and cost recovery: What happens after construction?

WATER AND SANITATION PROJECTS are known to bring wider economic benefits to communities in the form of health, opportunities for women and poverty reduction. Given the overall societal gains that can be achieved, water and sanitation services should be improved, especially for the poor. However, the challenge to finance new projects and increase sustainable access to water and sanitation services is particularly acute, largely due to lack of ability to generate funds for operations, maintenance, expansions and upgrades, coupled with insufficient institutional and administrative capacity

Micro finance for water and sanitation in West Africa

This paper focus on the challenge of financing the expansion and maintenance of water and sanitation services in poor rural areas and small towns. One possible solution lies in increasing flows of local finance through innovative financing mechanisms. These mechanisms must not only be available, but also be accessible to those who most need them. There is therefore a need to identify what capacities and support are required at local level to create the optimal conditions to promote and implement such mechanisms. The paper is based on a study undertaken by CREPA - Centre Régional pour l’Eau Potable et l’Assainissement à faible coût - in eight countries in West Africa which evaluated the impact and documented a number of experiences where micro finance has been used for water and sanitation projects

Grapevine candidate reference gene stability rankings during different treatments according to geNorm, NormFinder and BestKeeper.

<p>SV, stability value; CC, Pearson coefficient of correlation.</p><p>Grapevine candidate reference gene stability rankings during different treatments according to geNorm, NormFinder and BestKeeper.</p

Differential gene expression of <i>PAL</i> in grapevine leaves induced by (a) wounding and (b) UV-C irradiation.

<p>Relative gene expression quantification was performed using four different normalization factors derived from: the combination of the two top ranked genes, the best ranked gene, the second most stable gene and the worst ranked gene. All values are mean±SD (n = 4). Statistical differences (one-way ANOVA followed by Dunnett's multiple comparison test) to UBC+VAG – wounding or UBC+PEP – UV-C irradiation are marked: ns – not significant (<i>P</i>>0.05); * – significant (0.01<<i>P</i><0.05); ** – very significant (0.001<<i>P</i><0.01).</p

Candidate genes and primer pairs for <i>q</i>RT-PCR normalization in grapevine samples.

a<p>Powdery mildew (<i>Erysiphe necator</i>).</p>b<p>Not determined.</p>c<p><i>Phaeomoniella chlamydospora</i>.</p>d<p>Responsive gene used for differential expression quantification.</p><p>Candidate genes and primer pairs for <i>q</i>RT-PCR normalization in grapevine samples.</p

Consensus stability rankings generated by Monte Carlo algorithm for (a) leaf infection with <i>E. necator</i>, (b) leaf wounding, (c) leaf irradiation with UV-C, and (d) wood infection with <i>P. chlamydospora</i>.

<p>RankAggreg (v. 0.4–3) package for R was used to compute Monte Carlo algorithm with the Spearman footrule distances on the rank lists generated by each applet. Individual stability measurements (geNorm, NormFinder or BestKeeper) are shown in grey, average rank positions in black and the computed Monte Carlo model in red.</p

Expression profile of candidate normalization genes in grapevine samples during (a) leaf infection with <i>E. necator</i>, (b) leaf wounding, (c) leaf irradiation with UV-C and (d) wood infection with <i>P. chlamydospora</i>.

<p>Absolute <i>Ct</i> values for each treatment and the corresponding controls were combined. Each sample group comprises 5 to 7 biological replicates. The boxes indicate the 25^th and 75^th percentiles. Lines within the boxes represent the median. Maximum and minimum values are represented by wiskers.</p