Climate governance leverages collaborative climate change adaptation actions. Despite many joint actions, gaps exist between policy and practice in climate adaptation among East African countries. Climate governance in East Africa takes cues from the state’s politics and policy structures underpinned by its level of political transformation. As such, climate governance confronts challenges common to all other policy sectors that integrate a complex mix of economic resource allocation and variables akin to the management of public affairs. This chapter identifies prebendalism, nepotism and political patronage in resource allocations. This chapter assesses the trends in climate change governance in East Africa. It addresses climate change policy frameworks on adaptation at regional, national and local governments, gaps between policy and practice of the nationally determined contributions (NDCs) and the management of climate-related conflicts. The chapter concludes by recommending further courses of action to strengthen climate governance among the local communities.

Understanding thermal gradients is essential for sustainability of built-up ecosystems, biodiversity conservation, and human health. Urbanized environments in the tropics have received little attention on underlying factors and processes governing thermal variability as compared to temperate environments, despite the worsening heat stress exposure from global warming. This study characterized near surface air temperature (NST) and land surface temperature (LST) profiles across Kenyatta University, main campus, located in the peri-urban using in situ traverse temperature measurements and satellite remote sensing methods respectively. The study sought to; (i) find out if the use of fixed and mobile temperature sensors in time-synchronized in situ traverses can yield statistically significant temperature gradients (ΔT) attributable to landscape features, (ii) find out how time of the day influences NST gradients, (iii) determine how NST clusters compare to LST values derived from analysis of ‘cloud-free’ Landsat 8 OLI (Operational Land Imager) satellite image, and (iv) determine how NST and LST values are related to biophysical properties of land cover features.. The Getis–Ord Gi* statistics of ΔT values indicate statistically significant clustering hot and cold spots, especially in the afternoon (3–5 PM). NST ‘hot spots’ and ‘cold spots’ coincide with hot and cold regions of Landsat-based LST map. Ordinary Least Square Regression (OLS) indicate statistically significant (p < 0.01) coefficients of MNDWI and NDBI explaining 15% of ΔT variation, and albedo, MNDWI, and NDBI explaining 46% of the variations in LST patterns. These findings demonstrate that under clear sky, late afternoon walking traverses records spatial variability in NST within tropical peri-urban environments during dry season. This study approach may be enhanced through collecting biophysical attributes and NST records simultaneously to improve reliability of regression models for urban thermal ecology.